Method and device for face liveness detection

ABSTRACT

A method for face liveness detection and a device for face liveness detection. The method for face liveness detection includes: performing an illumination liveness detection and obtaining an illumination liveness detection result; and determining whether or not a face to be verified passes the face liveness detection at least according to the illumination liveness detection result. Performing of the illumination liveness detection and obtaining of the illumination liveness detection result includes: acquiring a plurality of illumination images of the face to be verified, in which the plurality of illumination images are captured in a process of dynamically changing mode of illumination light irradiated on the face to be verified and are respectively corresponding to various modes of the illumination light; and obtaining the illumination liveness detection result according to a light reflection characteristic of the face to be verified in the plurality of illumination images.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application claims priority to the Chinese patent application No.201611162606.0 filed on Dec. 15, 2016, the Chinese patent applicationNo. 201710161685.1 filed on Mar. 17, 2017, and the Chinese patentapplication No. 201710518028.8 filed on Jun. 29, 2017, the entiredisclosures of the above-mentioned Chinese patent applications areincorporated herein by reference as part of the present application.

TECHNICAL FIELD

Embodiments of the present disclosure relate to face recognition field,in particular relate to a method for face liveness detection and adevice for face liveness detection.

BACKGROUND

Face-based authentication systems have been widely applied currently.Along with the popularization of the face-based authentication systems,methods for maliciously attacking the face-based authentication systemsare emerged.

For initial versions of face-based authentication system, anauthentication result is obtained by comparing a face photo capturedduring an authentication process and a pre-stored face photo. However,for an authentication system based on face photo comparison, successfulauthentication can be realized in a case that a photo of a person beingcounterfeited is placed in front of a camera of the authenticationsystem based on face photo comparison. In other words, malicious userscan use the photo of the person being counterfeited for malicious attack(namely photo attack), and the authentication system based on face photocomparison cannot counteract photo attack.

SUMMARY

At least one embodiment of the present disclosure provides a method forface liveness detection, which comprises: performing an illuminationliveness detection and obtaining an illumination liveness detectionresult; and determining whether or not a face to be verified passes theface liveness detection at least according to the illumination livenessdetection result. Performing of the illumination liveness detection andobtaining of the illumination liveness detection result comprise:acquiring a plurality of illumination images of the face to be verified,in which the plurality of illumination images are captured in a processof dynamically changing mode of illumination light irradiated on theface to be verified and are respectively corresponding to various modesof the illumination light; and obtaining the illumination livenessdetection result according to a light reflection characteristic of theface to be verified in the plurality of illumination images.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, acquiring of theplurality of illumination images of the face to be verified comprises:dynamically changing the mode of the illumination light irradiated onthe face to be verified, and capturing the plurality of illuminationimages, which are respectively corresponding to the various modes of theillumination light, of the face to be verified; obtaining of theillumination liveness detection result according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images comprises: analyzing the plurality of illuminationimages, acquiring the light reflection characteristic of the face to beverified in the plurality of illumination images, and obtaining theillumination liveness detection result according to the light reflectioncharacteristic; and dynamically changing the mode of the illuminationlight irradiated on the face to be verified comprises: dynamicallychanging color and/or position of the illumination light.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, light emitted from adisplay screen is used as the illumination light irradiated on the faceto be verified; and a mode of the light emitted from the display screenis dynamically changed by changing contents displayed on the displayscreen, so that the mode of the illumination light irradiated on theface to be verified is dynamically changed.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: performing an action livenessdetection before determining whether or not the face to be verifiedpasses the face liveness detection. Performing of the action livenessdetection comprises: outputting an action instruction used for notifyingthe face to be verified to execute an action corresponding to the actioninstruction; acquiring an action image of the face to be verified;detecting the action executed by the face to be verified on the basis ofthe action image, so as to obtain an action detection result; andobtaining an action liveness detection result according to the actiondetection result and the action instruction. Determining of whether ornot the face to be verified passes the face liveness detection at leastaccording to the illumination liveness detection result comprises:determining whether or not the face to be verified passes the faceliveness detection according to both of the illumination livenessdetection result and the action liveness detection result.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, number of times forperforming the action liveness detection is increased by one for eachperformance of the action liveness detection, so as to obtain the numberof times for performing the action liveness detection; and after theaction liveness detection result is obtained and in a case that theaction liveness detection result indicates that the face to be verifiedfails to pass the action liveness detection, the method furthercomprises: outputting first error information used for notifying afailure of the action liveness detection; determining whether or not thenumber of times for performing the action liveness detection is greaterthan a first counting threshold; and determining whether or not the faceto be verified passes the face liveness detection at least according tothe illumination liveness detection result in a case that the number oftimes for performing the action liveness detection is greater than thefirst counting threshold, and performing the action liveness detectionagain in a case that the number of times for performing the actionliveness detection is not greater than the first counting threshold, orperforming the illumination liveness detection again in a case that theillumination liveness detection is performed before the action livenessdetection and the number of times for performing the action livenessdetection is not greater than the first counting threshold.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: determining whether or not apreset requirement of an image acquisition condition of the face to beverified is satisfied before performing the illumination livenessdetection, so as to perform the illumination liveness detection in acase that the preset requirement of the image acquisition condition issatisfied, in which the image acquisition condition at least comprisesone or more selected from a position of the face to be verified, a poseof the face to be verified and a size of the face to be verified in areal-time image acquired by an image acquisition device.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the image acquisitioncondition further comprises a blurriness of the real-time image and ashielding state of the face to be verified in the real-time image.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the preset requirement of the image acquisition condition of theface to be verified is satisfied comprises: adopting the imageacquisition device to acquire the real-time image of the face to beverified; displaying a reference part of the face to be verified in thereal-time image and a reference region in real time; and determiningwhether or not the preset requirement of the image acquisition conditionis satisfied at least according to whether or not the reference part ofthe face to be verified in the real-time image falls within thereference region.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the reference part ofthe face to be verified in the real-time image and the reference regionare respectively the face to be verified in the real-time image and theface preview region; or the reference part of the face to be verified inthe real-time image and the reference region are respectively a specificfacial part of the face to be verified in the real-time image and atargeted part region.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the preset requirement of the image acquisition condition issatisfied at least according to whether or not the reference part of theface to be verified in the real-time image falls within the referenceregion comprises: determining that the preset requirement of the imageacquisition condition is satisfied in a case that the reference part ofthe face to be verified in the real-time image falls within thereference region; and determining that the preset requirement of theimage acquisition condition is not satisfied in a case that thereference part of the face to be verified in the real-time image failsto fall within the reference region.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the preset requirement of the image acquisition condition issatisfied at least according to whether or not the reference part of theface to be verified in the real-time image falls within the referenceregion comprises: determining that the preset requirement of the imageacquisition condition is satisfied in a case that the reference part ofthe face to be verified in the real-time image falls within thereference region and a ratio of a size of the reference part of the faceto be verified to a size of the real-time image is greater than a ratiothreshold; and determining that the preset requirement of the imageacquisition condition is not satisfied in a case that the reference partof the face to be verified in the real-time image fails to fall withinthe reference region or the ratio of the size of the reference part ofthe face to be verified to the size of the real-time image is less thanor equal to the ratio threshold.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the preset requirement of the image acquisition condition of theface to be verified is satisfied further comprises: acquiring postureinformation of the image acquisition device; determining whether or notthe image acquisition device is vertically placed according to theposture information; and determining that the preset requirement of theimage acquisition condition is not satisfied in a case that the imageacquisition device is not vertically placed.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: outputting adjustment promptinformation in a case that the preset requirement of the imageacquisition condition is not satisfied, in which the adjustment promptinformation is used for notifying the face to be verified to makeadjustment allowing the preset requirement of the image acquisitioncondition to be satisfied.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, outputting of theadjustment prompt information in the case that the preset requirement ofthe image acquisition condition is not satisfied comprises: outputtingthe adjustment prompt information in a case that there is no face to beverified in the real-time image, so as to notify a person having theface to be verified to move toward a direction allowing the face to beverified to be in the real-time image; outputting the adjustment promptinformation in a case that the position of the face to be verified inthe real-time image is deviated from a face preview region, so as tonotify the person having the face to be verified to move towards adirection opposite to a deviation direction; outputting the adjustmentprompt information in a case that a blurriness of the real-time imageexceeds a preset blurriness threshold, so as to notify user to clean theimage acquisition device; outputting the adjustment prompt informationin a case that the pose of the face to be verified in the real-timeimage is in a face upward state, so as to notify the person having theface to be verified to lower his/her head; outputting the adjustmentprompt information in a case that the pose of the face to be verified inthe real-time image is in a face downward state, so as to notify theperson having the face to be verified to raise his/her head; outputtingthe adjustment prompt information in a case that the pose of the face tobe verified in the real-time image is tilting to the left or the right,so as to notify the person having the face to be verified to lookstraight ahead; outputting the adjustment prompt information in a casethat the size of the face to be verified in the real-time image is lessthan a first threshold, so as to notify the person having the face to beverified to be closer to the image acquisition device; outputting theadjustment prompt information in a case that the size of the face to beverified in the real-time image is greater than a second threshold, soas to notify the person having the face to be verified to be away fromthe image acquisition device; and outputting the adjustment promptinformation in a case that a specific facial part of the face to beverified in the real-time image is shielded by an occlusion, so as tonotify the person having the face to be verified to remove the occlusionand to expose the specific facial part.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the preset requirement of the image acquisition condition of theface to be verified is satisfied comprises: adopting the imageacquisition device to acquire the real-time image of the face to beverified; displaying a simulated face region changing along with theface to be verified in real time according to the image acquisitioncondition, in which the face to be verified is displayed in thesimulated face region; displaying in real time a targeted face region,which is used for indicating an alignment of the face to be verified;and determining whether or not the simulated face region is aligned withthe targeted face region, in which it is determined that the presetrequirement of the image acquisition condition of the face to beverified is satisfied in a case that the simulated face region isaligned with the targeted face region, and it is determined that thepreset requirement of the image acquisition condition of the face to beverified is not satisfied in a case that the simulated face region isnot aligned with the targeted face region.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the simulated face region is aligned with the targeted faceregion comprises: determining that the simulated face region is alignedwith the targeted face region in a case that the simulated face regionis within the targeted face region and a ratio of a size of thesimulated face region to a size of the real-time image is greater than afirst preset ratio; and determining that the simulated face region isnot aligned with the targeted face region in a case that the simulatedface region is not within the targeted face region or the ratio of thesize of the simulated face region to the size of the real-time image isless than or equal to the first preset ratio.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining of whetheror not the simulated face region is aligned with the targeted faceregion comprises: determining that the simulated face region is alignedwith the targeted face region in a case that the simulated face regionis within the targeted face region and a ratio of a size of thesimulated face region to a size of the targeted face region is greaterthan a second preset ratio; and determining that the simulated faceregion is not aligned with the targeted face region in a case that thesimulated face region is not within the targeted face region or theratio of the size of the simulated face region to the size of thetargeted face region is less than or equal to the second preset ratio.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: outputting first promptinformation during and/or before acquiring the real-time image of theface to be verified with the image acquisition device, in which thefirst prompt information is used for notifying the face to be verifiedto be directly opposite to the image acquisition device and to be closerto the image acquisition device.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: outputting second promptinformation during and/or before acquiring the illumination images, inwhich the second prompt information is used for notifying the face to beverified to keep still within a preset time period of the illuminationliveness detection.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, determining whether ornot the preset requirement of the image acquisition condition of theface to be verified is satisfied again in a case that the face to beverified moves during acquiring the illumination images and a movingdistance is beyond an allowable range.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, number of times forperforming the illumination liveness detection is increased by one foreach performance of the illumination liveness detection, so as to obtainthe number of times for performing the illumination liveness detection;and after the illumination liveness detection result is obtained, and ina case that the illumination liveness detection result indicates thatthe face to be verified fails to pass the illumination livenessdetection, the method further comprises: outputting second errorinformation used for notifying a failure of the illumination livenessdetection; determining whether or not the number of times for performingthe illumination liveness detection is greater than a second countingthreshold; and determining whether or not the face to be verified passesthe face liveness detection at least according to the illuminationliveness detection result in a case that the number of times forperforming the illumination liveness detection is greater than thesecond counting threshold, and determining whether or not the presetrequirement of the image acquisition condition of the face to beverified is satisfied or performing the illumination liveness detectionagain in a case that the number of times for performing the illuminationliveness detection is not greater than the second counting threshold.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, a predetermined patternis displayed on the display screen; and the contents displayed on thedisplay screen is changed by changing color and/or position of thepredetermined pattern.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the mode of theillumination light irradiated on the face to be verified is dynamicallychanged according to a predetermined rule or randomly.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, at an initial moment ofeach face liveness detection process, a unique identifier correspondingto this face liveness detection is generated.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: generating a time-varying realnumber sequence according to the unique identifier; and dynamicallychanging the mode of the illumination light irradiated on the face to beverified according to the real number sequence.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, acquiring of theplurality of illumination images, which are respectively correspondingto the various modes of the illumination light, of the face to beverified comprises: recording a video including the plurality ofillumination images.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, obtaining of theillumination liveness detection result according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images comprises: selecting same one part of the face to beverified in the plurality of illumination images; calculating lightreflection characteristics of the one part of the face to be verified inthe plurality of illumination images; and obtaining the illuminationliveness detection result according to the light reflectioncharacteristics of the one part of the face to be verified in theplurality of illumination images.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, obtaining of theillumination liveness detection result according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images comprises: selecting parts located at same oneposition of the plurality of illumination images; calculating lightreflection characteristics of the parts located at the one position ofthe plurality of illumination images; and obtaining the illuminationliveness detection result according to the light reflectioncharacteristics of the parts located at the one position of theplurality of illumination images.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, before selecting theparts located at the one position of the plurality of illuminationimages, the plurality of illumination images are aligned so that aposition of the face to be verified in the plurality of illuminationimages is substantially same.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, obtaining of theillumination liveness detection result according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images comprises: calculating a correlation between theplurality of illumination images and corresponding modes of theillumination light; and obtaining the illumination liveness detectionresult according to an calculation result of the correlation.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: determining whether or not theface to be verified is illuminated by bright light other than theillumination light during acquiring the plurality of illumination imagesbased on the plurality of illumination images; and notifying user tofind a location without the bright light to perform the face livenessdetection if the face to be verified is illuminated by the bright light.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the illuminationliveness detection result is obtained by a pre-trained classifier afterthe light reflection characteristic of the face to be verified in theplurality of illumination images is acquired.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, obtaining of the actionliveness detection result according to the action detection result andthe action instruction comprises: determining that the face to beverified passes the action liveness detection in a case that an action,which is executed by the face to be verified and matched with the actioninstruction, is detected in the action image, which is acquired within atime period not greater than a preset time period of the action livenessdetection, and determining that the face to be verified fails to passesthe action liveness detection in a case that the action, which isexecuted by the face to be verified and matched with the actioninstruction, is not detected in the action image, which is acquiredwithin the time period not greater than the preset time period of theaction liveness detection.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: outputting first time promptinformation during outputting the action instruction, in which the firsttime prompt information comprises count-down information correspondingto the preset time period of the action liveness detection.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the first promptinformation comprises one or more selected from voice information, imageinformation and text information.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the adjustment promptinformation comprises one or more selected from voice information, imageinformation and text information.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the adjustment promptinformation is displayed in an area above a face preview region.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the second promptinformation comprises count-down information corresponding to the presettime period of the illumination liveness detection.

For example, for the method for face liveness detection provided by atleast one embodiment of the present disclosure, the method for faceliveness detection further comprises: outputting a brightness controlinstruction during and/or before acquiring the real-time image of theface to be verified, in which the brightness control instruction isconfigured for setting a brightness of a display screen to be one of thefollowing: to be a constant value which is greater than a presetbrightness threshold but less than a maximum brightness of the displayscreen, to be a constant value which is equal to the maximum brightnessof the display screen, and to be changed along with an intensity changeof ambient light.

At least one embodiment of the present disclosure provides anothermethod for face liveness detection, which comprises: determining whetheror not a preset requirement of an image acquisition condition of a faceto be verified is satisfied, wherein the image acquisition condition atleast comprises one or more selected from a position of the face to beverified, a pose of the face to be verified and a size of the face to beverified, in a real-time image acquired by an image acquisition device;obtaining face images of the face to be verified in a case that thepreset requirement of the image acquisition condition of the face to beverified is satisfied; and determining whether or not the face to beverified passes the face liveness detection according to the faceimages.

At least one embodiment of the present disclosure provides a device forface liveness detection, which comprises: a light source, an imageacquisition device and a processing device, in which the light source isconfigured to dynamically change mode of illumination light irradiatedon a face to be verified; the image acquisition device is configured toacquire a plurality of illumination images, which are respectivelycorresponding to various modes of the illumination light, of the face tobe verified; and the processing device is configured to obtain anillumination liveness detection result according to a light reflectioncharacteristic of the face to be verified in the plurality ofillumination images, and is further configured to determine whether ornot the face to be verified passes the face liveness detection at leastaccording to the illumination liveness detection result.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the device for faceliveness detection further comprises an output device, in which theoutput device is configured to output an action instruction, in whichthe action instruction is used for notifying the face to be verified toexecute an action corresponding to the action instruction; the imageacquisition device is further configured to acquire an action image ofthe face to be verified; the processing device is further configured toobtain an action detection result by detecting the action executed bythe face to be verified on the basis of the action image, and obtain anaction liveness detection result according to the action detectionresult and the action instruction; and the processing device is furtherconfigured to determine whether or not the face to be verified passesthe face liveness detection according to both of the illuminationliveness detection result and the action liveness detection result.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the device for faceliveness detection further comprises: a condition determination device,in which the image acquisition device is further configured to acquire areal-time image; the condition determination device is configured todetermine whether or not an preset requirement of the image acquisitioncondition of the face to be verified is satisfied before acquiring theillumination images; and the image acquisition condition at leastcomprises one or more selected from a position of the face to beverified, a pose of the face to be verified and a size of the face to beverified in the real-time image acquired by the image acquisitiondevice.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the light source isconfigured to dynamically change the mode of the illumination lightirradiated on the face to be verified by dynamically changing colorand/or position of the illumination light.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the light source is adisplay screen; and the display screen is configured to dynamicallychange the mode of the illumination light irradiated on the face to beverified by changing contents displayed on the display screen.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the display screen isconfigured to display a predetermined pattern and dynamically changecolor and/or position of the predetermined pattern.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the light source isconfigured to dynamically change the mode of the illumination lightirradiated on the face to be verified according to a predetermined ruleor randomly.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, at an initial moment ofeach face liveness detection process, the processing device is furtherconfigured to generate a unique identifier corresponding to this faceliveness detection.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to generate a time-varying real number sequenceaccording to the unique identifier, and dynamically change the mode ofthe illumination light irradiated on the face to be verified accordingto the real number sequence.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the image acquisitiondevice is configured to record a video so as to acquire the plurality ofillumination images, which are respectively corresponding to the variousmodes of the illumination light, of the face to be verified.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to select same one part of the face to be verified inthe plurality of illumination images, calculate the light reflectioncharacteristics of the one part of the face to be verified in theplurality of illumination images, and obtain the illumination livenessdetection result according to the light reflection characteristics ofthe one part of the face to be verified in the plurality of illuminationimages.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to select parts located at same one position of theplurality of illumination images, calculate light reflectioncharacteristics of the parts located at the one position of theplurality of illumination images, and obtain the illumination livenessdetection result according to the light reflection characteristics ofthe parts located at the one position of the plurality of illuminationimages.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to align the plurality of illumination images beforeselecting the parts located at the one position of the plurality ofillumination images, so that a position of the face to be verified inthe plurality of illumination images is substantially same.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to: calculate a correlation between the plurality ofillumination images and corresponding modes of the illumination light,and obtaining the illumination liveness detection result according to ancalculation result of the correlation.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isfurther configured to determine whether or not the face to be verifiedis illuminated by bright light other than the illumination light duringacquiring the plurality of illumination images based on the plurality ofillumination images, and prompt user to find a location without thebright light to perform the face liveness detection if the face to beverified is illuminated by the bright light.

For example, for the device for face liveness detection provided by atleast one embodiment of the present disclosure, the processing device isconfigured to obtain the illumination liveness detection result by apre-trained classifier after the light reflection characteristic of theface to be verified in the plurality of illumination images is acquired.

At least one embodiment of the present disclosure further providesanother device for face liveness detection, which comprises: aprocessor, a memory and computer program instructions stored in thememory; in which upon the processor running the computer programinstructions, the device for face liveness detection performs afollowing method comprising: acquiring a plurality of illuminationimages of a face to be verified, in which the plurality of illuminationimages are captured in a process of dynamically changing mode ofillumination light irradiated on the face to be verified and arerespectively corresponding to various modes of the illumination light;obtaining an illumination liveness detection result according to a lightreflection characteristic of the face to be verified in the plurality ofillumination images; and determining whether or not the face to beverified passes the face liveness detection at least according to theillumination liveness detection result.

At least one embodiment of the present disclosure further providesfurther another device for face liveness detection, which comprises alight source, an image acquisition device, an information transmitterand receiver and a processing device. The light source is configured todynamically change mode of illumination light irradiated on a face to beverified; the image acquisition device is configured to acquire aplurality of illumination images, which are respectively correspondingto various modes of the illumination light, of the face to be verified;the information transmitter and receiver is configured to send theplurality of illumination images of the face to be verified to a server,and receive an illumination liveness detection result, which is obtainedaccording to a light reflection characteristic of the face to beverified in the plurality of illumination images, from the server; andthe processing device is configured to determine whether or not the faceto be verified passes the face liveness detection at least according tothe illumination liveness detection result.

At least one embodiment of the present disclosure further provides stillanother device for face liveness detection, which comprises aninformation transmitter and receiver and a processing device. Theinformation transmitter and receiver is configured to receive aplurality of illumination images of a face to be verified from a client;the processing device is configured to obtain an illumination livenessdetection result according to a light reflection characteristic of theface to be verified in the plurality of illumination images; and theinformation transmitter and receiver is further configured to send theillumination liveness detection result, which is obtained by theprocessing device according to the light reflection characteristic ofthe face to be verified in the plurality of illumination images, to theclient.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to clearly illustrate the technical solution of the embodimentsof the disclosure, the drawings of the embodiments will be brieflydescribed in the following; it is obvious that the described drawingsare only related to some embodiments of the disclosure and thus are notlimitative of the disclosure.

FIG. 1A is an illustrative block diagram of a device for face livenessdetection provided by an embodiment of the present disclosure;

FIG. 1B is a schematic diagram of the device for face liveness detectionas illustrated in FIG. 1A;

FIG. 2A is a schematic diagram to illustrate one configuration of afirst area of a display screen of the device for face liveness detectionprovided by an embodiment of the present disclosure;

FIG. 2B is a schematic diagram to illustrate another configuration ofthe first area of the display screen of the device for face livenessdetection provided by an embodiment of the present disclosure;

FIG. 2C is a schematic diagram to illustrate further anotherconfiguration of the first area of the display screen of the device forface liveness detection provided by an embodiment of the presentdisclosure;

FIG. 2D is a schematic diagram to illustrate still another configurationof the first area of the display screen of the device for face livenessdetection provided by an embodiment of the present disclosure;

FIG. 3A is a schematic diagram to illustrate one method of dynamicallychanging position of illumination light irradiated on the face to beverified in an embodiment of the present disclosure;

FIG. 3B is a schematic diagram to illustrate another method ofdynamically changing the position of the illumination light irradiatedon the face to be verified in an embodiment of the present disclosure;

FIG. 3C is a schematic diagram to illustrate further another method ofdynamically changing the position of the illumination light irradiatedon the face to be verified in an embodiment of the present disclosure;

FIG. 4 is a schematic diagram to illustrate one method of dynamicallychanging color of the illumination light irradiated on the face to beverified in an embodiment of the present disclosure;

FIG. 5A is a schematic diagram to illustrate one method of dynamicallychanging the position and the color of the illumination light irradiatedon the face to be verified in an embodiment of the present disclosure;

FIG. 5B is a schematic diagram to illustrate another method ofdynamically changing the position and the color of the illuminationlight irradiated on the face to be verified in an embodiment of thepresent disclosure;

FIG. 6A is a schematic diagram to illustrate an illustrative scene ofadopting an image acquisition device to acquire a plurality ofillumination images, which are respectively corresponding to variousmodes, of the face to be verified in an embodiment of the presentdisclosure;

FIG. 6B is a schematic diagram to illustrate another illustrative sceneof adopting the image acquisition device to acquire the plurality ofillumination images, which are respectively corresponding to variousmodes, of the face to be verified in an embodiment of the presentdisclosure;

FIG. 7 is a flow diagram to illustrate an illustrative method ofadopting a processing device to determine whether or not the face to beverified is a face of living human according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images in an embodiment of the present disclosure;

FIG. 8A is an illustrative block diagram of another device for faceliveness detection provided by an embodiment of the present disclosure;

FIG. 8B is a schematic diagram of the device for face liveness detectionas illustrated in FIG. 8A;

FIG. 9 is an illustrative block diagram of further another device forface liveness detection provided by an embodiment of the presentdisclosure;

FIG. 10 is an illustrative block diagram of still another device forface liveness detection provided by an embodiment of the presentdisclosure;

FIG. 11 is an illustrative block diagram of a device for face livenessdetection provided by another embodiment of the present disclosure;

FIG. 12 is an illustrative block diagram of a device for face livenessdetection provided by further another embodiment of the presentdisclosure;

FIG. 13 is an illustrative block diagram of a device for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 14 is an illustrative block diagram of a device for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 15 is an illustrative block diagram of a device for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 16 is an illustrative flow diagram of a method for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 17 is an illustrative flow diagram illustrating a method forobtaining illumination detection result in still another embodiment ofthe present disclosure;

FIG. 18 is an illustrative flow diagram of a method for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 19 is an illustrative flow diagram of another method for faceliveness detection provided by still another embodiment of the presentdisclosure;

FIG. 20 is an illustrative schematic diagram of a method for faceliveness detection provided by still another embodiment;

FIG. 21 is an illustrative flow diagram of a method for face livenessdetection provided by still another embodiment;

FIG. 22 is an illustrative flow diagram of another method for faceliveness detection provided by still another embodiment;

FIG. 23 is an illustrative flow diagram of further another method forface liveness detection provided by still another embodiment;

FIG. 24 is a schematic diagram illustrating displayed contents on adisplay screen in a process of implementing the method for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 25 is a schematic diagram illustrating displayed content on thedisplay screen in a process of implementing the method for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 26 is a schematic diagram illustrating still another displayedcontent on the display screen in the process of implementing the methodfor face liveness detection provided by still another embodiment of thepresent disclosure;

FIG. 27 is an illustrative block diagram of a device for face livenessdetection provided by still another embodiment of the presentdisclosure;

FIG. 28 is an illustrative block diagram of another device for faceliveness detection provided by still another embodiment of the presentdisclosure;

FIG. 29 is an illustrative block diagram of further another device forface liveness detection provided by still another embodiment of thepresent disclosure;

FIG. 30 is an illustrative block diagram of still another device forface liveness detection provided by still another embodiment of thepresent disclosure;

FIG. 31 is an illustrative block diagram of a device for face livenessdetection provided by still another embodiment of the presentdisclosure; and

FIG. 32 is an illustrative block diagram of another device for faceliveness detection provided by still another embodiment of the presentdisclosure.

DETAILED DESCRIPTION

In order to make objects, technical details and advantages of theembodiments of the disclosure apparent, the technical solutions of theembodiments will be described in connection with the drawings related tothe embodiments of the disclosure. Apparently, the described embodimentsare just a part but not all of the embodiments of the disclosure. Basedon the described embodiments herein, those skilled in the art can obtainother embodiment(s), without any inventive work, which should be withinthe scope of the disclosure.

Unless otherwise defined, all the technical and scientific terms usedherein have the same meanings as commonly understood by one of ordinaryskill in the art to which the present disclosure belongs. The terms“first,” “second,” etc., which are used in the description and theclaims of the present application for disclosure, are not intended toindicate any sequence, amount or importance, but distinguish variouscomponents. Also, the terms such as “a,” “an,” etc., are not intended tolimit the amount, but indicate the existence of at least one. The terms“comprise,” “comprising,” “include,” “including,” etc., are intended tospecify that the elements or the objects stated before these termsencompass the elements or the objects and equivalents thereof listedafter these terms, but do not preclude the other elements or objects.The phrases “connect”, “connected”, etc., are not intended to define aphysical connection or mechanical connection, but may include anelectrical connection, directly or indirectly. “On,” “under,” “right,”“left” and the like are only used to indicate relative positionrelationship, and when the position of the object which is described ischanged, the relative position relationship may be changed accordingly.

The accompanying drawings are provided for better understanding of theembodiments of the present disclosure, and are part of the description.The accompanying drawings are used for illustrating the presentdisclosure together with the embodiments of the present disclosure, andare not intended to limit the present disclosure. In the accompanyingdrawings, same reference numerals generally represent same components orsteps.

The authentication system based on face photo comparison can be improvedfor solving the problem of photo attack regarding the face-basedauthentication system. In an improved face-based authentication system,the above-mentioned photo attack can be coped effectively throughdetermining whether or not a fine action on the face is detected.Moreover, the user can be asked to do specific action, so as to enhanceresistance against the photo attack regarding the face-basedauthentication system. However, malicious users (i.e., maliciousattackers) still can use tablet PCs, smart terminals and the like toplay video or animation to counterfeit a face action of a person beingcounterfeited, so as to perform malicious attack (namely animationattack). The video or animation for counterfeiting the face action ofthe person being counterfeited can be synthesized by photos of theperson being counterfeited or can be real videos of the person beingcounterfeited, obtained by attackers via various means. As a result,such video or animation is highly deceptive, and the authenticationsystem based on face action is sometimes difficult to cope with suchanimation attack. Therefore, the industry is still looking for themethod and device for performing more effective and convenient faceliveness detection in face recognition processes.

For example, the embodiments of the present disclosure provide a methodfor face liveness detection and a device for face liveness detection.For example, the method and device for face liveness detection candetermine whether or not a face to be verified passes the face livenessdetection at least according to an illumination liveness detectionresult, and hence can effectively counteract malicious attack executedby malicious users by using photos or videos of the person beingcounterfeited. For example, the method and device for face livenessdetection can be used in various authentication fields and similarfields (e.g., E-commerce, mobile payment and bank account opening).

At least one embodiment of the present disclosure provides a method forface liveness detection, which comprises: performing an illuminationliveness detection and obtaining an illumination liveness detectionresult; and determining whether or not a face to be verified passes theface liveness detection (i.e., passes the face liveness authentication)at least according to the illumination liveness detection result. Forexample, performing of the illumination liveness detection and obtainingof the illumination liveness detection result comprise: acquiring aplurality of illumination images of the face to be verified; andobtaining the illumination liveness detection result according to alight reflection characteristic of the face to be verified in theplurality of illumination images. For example, the plurality ofillumination images are captured in a process of dynamically changingmode of illumination light irradiated on the face to be verified and arerespectively corresponding to various modes of the illumination light.

In one example, the illumination liveness detection result can bedirectly used as the final face liveness detection result. That is tosay, if the illumination liveness detection result indicates that theface to be verified pass the illumination liveness detection, it isdetermined that the face to be verified passes the face livenessdetection and the face to be verified belongs to a living body; and ifthe illumination liveness detection result indicates that the face to beverified fails to pass the illumination liveness detection, it isdetermined that the face to be verified fails to pass the face livenessdetection and the face to be verified does not belong to a living body.This authentication method has advantages such as low calculation amountand high efficiency.

In another example, other face liveness detection methods or processescan be combined, such that whether or not the face to be verified passesthe face liveness detection is determined based on the illuminationliveness detection result and other liveness detection results obtainedon the basis of other liveness detection methods or processes. Thisauthentication method has advantages such as high accuracy.

At least one embodiment of the present disclosure provides a device forface liveness detection, which comprises: a processor, a memory andcomputer program instructions stored in the memory. Upon the processorrunning the computer program instructions, the device for face livenessdetection performs a following method including: acquiring a pluralityof illumination images of the face to be verified; obtaining anillumination liveness detection result according to a light reflectioncharacteristic of the face to be verified in the plurality ofillumination images; and determining whether or not the face to beverified passes the face liveness detection at least according to theillumination liveness detection result. The plurality of illuminationimages are captured in a process of dynamically changing mode ofillumination light irradiated on the face to be verified and arerespectively corresponding to various modes of the illumination light.In one example of the embodiment of the present disclosure, a pluralityof pre-stored illumination images of the face to be verified can beacquired from the memory. In another example of the embodiment of thepresent disclosure, the plurality of illumination images of the face tobe verified can also be received from other devices (e.g., received fromthe image acquisition device).

At least one embodiment of the present disclosure provides anotherdevice for face liveness detection, which can comprise components,devices, modules, sub-modules and/or units for implementing the methodfor face liveness detection provided by the embodiments of the presentdisclosure. More specifically, the device for face liveness detectioncan comprise a light source, an image acquisition device and aprocessing device. The light source is configured to dynamically changemode of illumination light irradiated on a face to be verified; theimage acquisition device is configured to acquire a plurality ofillumination images, which are respectively corresponding to variousmodes of the illumination light, of the face to be verified; theprocessing device is configured to obtain an illumination livenessdetection result according to a light reflection characteristic of theface to be verified in the plurality of illumination images, and isfurther configured to determine whether or not the face to be verifiedpasses the face liveness detection at least according to theillumination liveness detection result.

In different embodiments, for example, the processing device candetermine whether or not the face to be verified is a face of livinghuman and can determine whether or not the face to be verified passesthe face liveness detection only according to the illumination livenessdetection result; for another example, according to specificimplementation demands, the processing device can also determine whetheror not the face to be verified is a face of living human and whether ornot the face to be verified passes the face liveness detection on thebasis of the illumination liveness detection result and the actionliveness detection result. For example, the illumination livenessdetection can be executed before the action liveness detection. Foranother example, the illumination liveness detection can also beexecuted after the action liveness detection. No specific limitationwill be given here in the embodiment of the present disclosure.

For example, the device for face liveness detection can further comprisea condition determination device according to specific implementationdemands. Thus, whether or not a preset requirement of the imageacquisition condition of the face to be verified is satisfied can bedetermined by the condition determination device before performing theillumination liveness detection, and then the illumination livenessdetection can be performed in a case that the condition determinationdevice determines that the preset requirement of the image acquisitioncondition is satisfied. The image acquisition condition at leastcomprises one or more selected from a position of the face to beverified, a pose of the face to be verified and a size of the face to beverified, in a real-time image acquired by an image acquisition device.

For example, in different embodiments, for example, light-emittingdevices such as at least partial areas of a display screen or a lightsource in a projector can be used as the light source. For anotherexample, the light source can also be a special purpose light source(for example, one or more light-emitting diodes (LEDs) or laser diodesarranged according to a certain manner, or a flash lamp for a camera).For further another example, the light source can also be a combinationof the at least partial areas of the display screen and the specialpurpose light source.

For example, the mode of the illumination light irradiated on the faceto be verified can be dynamically changed by dynamically changing thecolor of the illumination light. For another example, the mode of theillumination light irradiated on the face to be verified can also bedynamically changed by dynamically changing the region, which isirradiated by the illumination light, of the face to be verified (namelyby dynamically changing the position of the illumination light). Forfurther another example, the mode of the illumination light irradiatedon the face to be verified can also be dynamically changed bysimultaneously and dynamically changing the color (i.e., wavelength) ofthe illumination light and the position of the illumination light.

For example, the position of the illumination light can be dynamicallychanged by changing a position of the light source. For another example,the position of the illumination light can also be dynamically changedby changing an angle of emitted light of the light source.

For example, the device for face liveness detection can have thefunction of obtaining the illumination liveness detection resultaccording to videos and/or images of the face to be verified, such thatwhether or not the face to be verified is a face of living human can bedetermined (for example, the device for face liveness detection candetermine whether or not the face to be verified is a face of livinghuman at least based on the illumination liveness detection result). Foranother example, the device for face liveness detection can also sendthe videos and/or images of the face to be verified to an externaldevice (for example, a server) and then receive the illuminationliveness detection result and/or determination information, which isobtained at least according to the illumination liveness detectionresult, regarding whether or not the face to be verified is a face ofliving human from the external device. For further another example, thedevice for face liveness detection can receive the videos and/or imagesof the face to be verified (for example, the illumination images),obtain the illumination liveness detection result and/or determinationinformation, which is obtained at least according to the illuminationliveness detection result, regarding whether or not the face to beverified is a face of living human, and then send the illuminationliveness detection result and/or determination information to theexternal device (e.g., a mobile terminal).

For example, the skin of a human face is a diffuse reflection material,and the human face is a three-dimensional (3D) object. In contrast, forexample, a display screen of an LCD or an OLED display can be regardedas a self-luminous object and light exited from the display screengenerally includes specular reflection component; for another example,photos and the like are generally a planar object and light exited fromthe photos and the like also generally include the specular reflectioncomponent. Furthermore, no matter the face to be verified is presentedby the display screen or by the photo, an overall reflectioncharacteristic is uniform and lack of the three-dimensionalcharacteristic of the human face. Therefore, a light reflectioncharacteristic of the human face is different from a light reflectioncharacteristic of the display screen or the photo, and thus whether ornot the face to be verified is a face of living human can be determinedat least according to the light reflection characteristic of the face tobe verified.

Nonrestrictive descriptions will be given below to different embodimentsof the present disclosure and specific examples thereof with referenceto the accompanying drawings. As described below, different features inthe following concrete embodiments can be combined to obtain newembodiments in a case that no conflict is existed, and these embodimentsshall also fall within the scope of the present disclosure.

For example, FIG. 1A is an illustrative block diagram of a device forface liveness detection 100 provided by an embodiment of the presentdisclosure, and FIG. 1B is a schematic diagram of the device for faceliveness detection 100 as illustrated in FIG. 1A. As illustrated inFIGS. 1A and 1B, the device for face liveness detection 100 comprises adisplay screen 110, an image acquisition device 120 and a processingdevice 130. For example, the image acquisition device 120 can bedisposed at a periphery region (e.g., an upper side region) of thedisplay screen 110. For example, the processing device 130 can bedisposed at a side of the display screen 110 away from thelight-emitting direction.

For example, the device for face liveness detection can be implementedinto a variety of forms. No limitation will be given here in theembodiment of the present disclosure.

For example, the device for face liveness detection can be a specialpurpose device intended for the face liveness detection. For anotherexample, the device for face liveness detection can also be a componentof other electronic devices (e.g., a mobile phone, a tablet PC, anotebook computer or smart glasses); the device for face livenessdetection can be, for example, a component of an entrance guard systemor a component of equipment such as an ATM. In such a case, the devicefor face liveness detection 100 does not need an additional light sourceand hence the volume, the weight and the cost of the device for faceliveness detection 100 can be reduced.

In one specific example, the device for face liveness detection 100 is acomponent of an electronic system (e.g., an authentication system or anelectronic trading system); the processing device 130 is an independentserver or server cluster separated from the face liveness detection 100;the display screen 110 and the image acquisition device 120 arerespectively a display screen of a mobile terminal (e.g., a smart mobilephone or a tablet PC) and a front camera disposed on a same side withthe display screen; and the processing device 130 is communicated withthe mobile terminal by wired or wireless means.

In the embodiment, at least partial area of the display screen 110 canbe used as the light source. For example, the display screen 110 can beconfigured to dynamically change mode of illumination light irradiatedon a face to be verified by changing contents displayed on the displayscreen 110 (namely changing position and/or color of a luminous area ofthe display screen 110). For example, the display screen 110 can beconfigured to display a predetermined pattern and dynamically changecolor and/or position of the predetermined pattern. Description will begiven below with reference to FIGS. 2-5 for illustrating how todynamically change the position and/or the color of the illuminationlight irradiated on the face to be verified by changing the contentsdisplayed on the display screen 110, so as to dynamically change themode of the illumination light irradiated on the face to be verified.

FIGS. 2A-2D illustrate feasible configurations of the light source bythe display screen 110. For example, the display screen 110 can includea first area 151 and a second area 152; at least partial area of thefirst area 151 is used as the light source; and the second area 152 ofthe display screen 110 can be used for displaying information helpingthe user to use the device for face liveness detection 100, e.g., promptinformation for operating the device for face liveness detection. Forexample, a display brightness of the first area 151 can be significantlyincreased, or a ratio of light with short wavelength (e.g., blue lightor green light) to light emitted by the first area 151 can besignificantly increased. For example, as illustrated in FIG. 2A, theentire display screen 110 can be set to be the first area 151. Foranother example, as illustrated in FIG. 2B, the first area 151 can bedisposed at a central area of the display screen 110. For furtheranother example, as illustrated in FIG. 2C, the first area 151 can alsobe disposed at an upper area of the display screen 110. For stillanother example, as illustrated in FIG. 2D, the first area 151 can alsobe disposed on the periphery of the display screen 110. Obviously, aposition and a shape of the first area 151 of the display screen 110,(i.e., the light source) are not limited to the configurations asillustrated in FIGS. 2A-2D; the first area 151 (i.e., the light source),for example, can also be disposed at an upper area, a left area or aright area of the display screen 110; and the shape of the first area151 (i.e., the light source) can also be circular, triangular,elliptical, etc. No limitation will be given here in the embodiment ofthe present disclosure.

FIG. 3A illustrates a method of dynamically changing the region, whichis irradiated by the illumination light, of the face to be verified(dynamically changing the position of the illumination light) providedby an embodiment of the present disclosure. For example, the displayscreen 110 can further include a guide section 153, and the guidesection 153 is used for guiding the user to align the eyes of the userwith preset positions 154 of the guide section 153. For example, theposition of the luminous area of the first area 151 can change withtime. As illustrated in FIG. 3A, at time T₁, the luminous area of thefirst area 151 is disposed at a first position [W₁], and the luminousarea includes columns (or a column) of display pixels of the first area151 at the first position; at time T₂, the luminous area of the firstarea 151 is disposed at a second position [W₂]; at time T_(n), theluminous area of the first area 151 is disposed at the N^(th) position[W_(N)]; and at time T_(m), the luminous area of the first area 151 isdisposed at the M^(th) position [W_(M)]. That is to say, the luminousarea can be dynamically changed along with time along a row direction ofthe first area 151. When the position of the luminous area of the firstarea 151 changes relative to the face to verified, the region, which isirradiated by the light emitted by the luminous area, of the face to beverified can also be changed correspondingly, therefore, the position ofthe illumination light can be dynamically changed, and hence the mode ofthe illumination light irradiated on the face to be verified can bedynamically changed.

Obviously, the method to dynamically change the luminous area of thefirst area 151 along with time is not limited to the case as illustratedin FIG. 3A and can also be the case as illustrated in FIGS. 3B and 3C.For example, as illustrated in FIG. 3B, the luminous area includes rows(or a row) of display pixels of the first area 151 at the N^(th)position [W_(N′)] at time T_(n), so the luminous area can be dynamicallychanged along with time along a column direction of the first area 151.For another example, as illustrated in FIG. 3C, the luminous areaincludes display pixels of the first area 151 disposed at the (N,N′)^(th) position [W_(N), W_(N′)] at time T_(n), so the luminous areacan also be dynamically changed along with time along the row directionand the column direction of the first area 151. For example, a changingfrequency of the position of the luminous area along the row directionand/or the column direction and the number of display pixels of theluminous area can be set according to specific implementation demands.No limitation will be given here in the embodiment of the presentdisclosure.

For example, FIG. 4 illustrates a method of dynamically changing thecolor of the illumination light irradiated on the face to be verifiedprovided by an embodiment of the present disclosure. For example, thecolor of the first area 151 at time T₁ can be a first color 161; thecolor at time T₂ can be a second color 162; the color at time T_(n) canbe an N^(th) color 163; and the color at time T_(m) can be an M^(th)color 164. That is to say, the color of the first area 151 can bedynamically changed along with time. The color of the illumination lightcan be dynamically changed by controlling the display screen 110 andallowing the color of the emitted light of the first area 151 to bedynamically changed with time, and hence the mode of the illuminationlight irradiated on the face to be verified can be dynamically changed.For example, a changing frequency of the color of the emitted light ofthe first area 151 and the size (or area) and the position of theluminous area of the first area 151 can be set according to specificimplementation demands. No limitation will be given here in theembodiment of the present disclosure.

For example, FIG. 5A illustrates a method of dynamically changing theposition and the color of the illumination light irradiated on the faceto be verified. As illustrated in FIG. 5A, at time T₁, the luminous areaof the first area 151 is disposed at the W_(1′) position, and the colorof the emitted light of the luminous area can be a first pattern color171; at time T₂, the luminous area of the first area 151 is disposed atthe W₂′ position, and the color of the emitted light of the luminousarea can be a second pattern color 172; at time T_(n), the luminous areaof the first area 151 is disposed at the W_(N)′ position, and the colorof the emitted light of the luminous area can be a N′^(th) pattern color173; and at time T_(m), the luminous area of the first area 151 isdisposed at the W_(M)′ position, and the color of the emitted light ofthe luminous area can be a M′^(th) pattern color 174. That is to say,the position and the color of the emitted light of the luminous area aredynamically changed along with time. Therefore, the position and thecolor of the illumination light can be dynamically changed bycontrolling the display screen 110 and allowing the position and thecolor of the emitted light of the luminous area of the first area 151 tobe dynamically changed along with time, and hence the mode of theillumination light irradiated on the face to verified can be dynamicallychanged. Obviously, the luminous area can also be dynamically changedalong with time along the column direction of the first area 151, oralong the row direction and the column direction of the first area 151.No further description will be given herein.

For example, FIG. 5B illustrates another method of dynamically changingthe position and the color of the illumination light provided anembodiment of the present disclosure. As illustrated in FIG. 5B, thefirst area 151 displays a first pattern 181 at time T₁, displays asecond pattern 182 at time T₂, displays an N^(th) pattern 183 at timeT_(n), and displays an M^(th) pattern 184 at time T_(M). For example,the colors displayed at different positions of the first area 151 followdifferent color change rules. Therefore, the position and the color ofthe illumination light can be dynamically changed, and hence the mode ofthe illumination light irradiated on the face to be verified can bedynamically changed. For example, the color change rules at differentpositions can be set according to specific implementation demands. Nospecific limitation will be given here in the embodiment of the presentdisclosure.

For example, the rule followed by the display screen 110 in dynamicallychanging the mode of the illumination light irradiated on the face to beverified can be selected according to specific implementation demands,for example, can be selected from a plurality of pre-stored modes. Nospecific limitation will be given here in the embodiment of the presentdisclosure.

For example, the display screen 110 can change the mode of theillumination light irradiated on the face to be verified according to apredetermined rule. For example, the position of the luminous area ofthe first area 151 can be dynamically changed according to a rule ofsequentially moving from leftmost of the display screen 110 to rightmostof the display screen 110. For another example, the color of theluminous area (i.e., the color of the emitted light of the luminousarea) of the first area 151 can be dynamically changed according to arule of sequentially displaying red, green and blue colors forrespectively 20 times per minute.

For example, the display screen 110 can also randomly and dynamicallychange the mode of the illumination light irradiated on the face to beverified. Description will be given below to an illustrative method ofrandomly and dynamically changing the mode of the illumination lightirradiated on the face to be verified by taking changing color as anexample. The illustrative method of randomly and dynamically changingthe mode of the illumination light irradiated on the face to be verifiedcan comprise the following steps.

S1: acquiring a unique identifier. For example, the unique identifiercan be acquired by the processing device 130 at an initial moment ofeach face liveness detection process. For example, the unique identifiercan be acquired from an external device (e.g., a cloud server) separatedfrom the device for face liveness detection 100, in such a case, theunique identifier can be acquired by a communication componentcontrolled by the processing device 130. For another example, the uniqueidentifier can also be generated by the processing device 130 of thedevice for face liveness detection 100. For example, the method ofgenerating the unique identifier by the device for face livenessdetection 100 can refer to random number generation technology, globalunique identifier generation technology, etc. No limitation will begiven here in the embodiment of the present disclosure. For example, theunique identifier can be invalidated immediately after a termination ofeach performance of the face liveness detection. For another example,the unique identifier can also be invalidated after the uniqueidentifier has been generated for a period of time (e.g., 3 minutes)even though current face liveness detection has not ended.

S2: generating a real number sequence P[i] with a length of T accordingto the unique identifier. The real number sequence corresponds to imageschanging over time. For example, P[i] corresponds to the i^(th) frame ofimage displayed by the display screen 110. The generation method of thereal number sequence can refer to the conventional cryptographicalgorithm, and no limitation will be given here in the embodiment of thepresent disclosure. For example, the real number sequence with thelength of T can be generated by hash algorithm. For example, the lengthof the real number sequence can be set according to specificimplementation demands, and no limitation will be given here. Forexample, the real number sequence P[i] can satisfy sum(P[i])=0, namelythe value obtained by summing the real number sequence P[i] can be zero.

S3: obtaining at least two different colors. For example, the at leasttwo different colors can be preset and stored in the device for faceliveness detection 100. For example, in a coordinate system (R, G, B) ofcolor space, supposing 0 is the minimum intensity and 255 is the maximumintensity, (0, 0, 255), (0, 255, 0), (255, 0, 0) and (128, 128, 128) canbe specified as predetermined colors and stored in the device for faceliveness detection 100. For another example, the at least two differentcolors can also be generated according to the unique identifier. Themethod of generating the at least two different colors according to theunique identifier can refer to the conventional cryptographic algorithm,and no further description will be given here. For further anotherexample, the at least two different colors can also be randomlygenerated. The specific method can refer to random number generationtechnology, and no further description will be given here.

S4: the RGB value of colors displayed by the display screen 100 atdifferent times is obtained according to the real number sequence andthe at least two different colors. For example, taking a case ofpresetting or generating two colors (r1, g1, b1) and (r2, g2, b2) as anexample, the color (r[i], g[i], b[i]) of the i^(th) frame of imagedisplayed by the display screen 110 can be as follows:r[i]=r1+(r2−r1)×P[i], g[i]=g1+(g2−g1)×P[i], b[i]=b1+(b2−b1)×P[i].Therefore, the color of the illumination light irradiated on the face tobe verified can be randomly and dynamically changed by performing theabove-mentioned steps. The position of the illumination light irradiatedon the face to be verified, or the color and the position of theillumination light irradiated on the face to be verified can also berandomly and dynamically changed by similar method. No furtherdescription will be given herein. Thus, the display screen 110 canrandomly and dynamically change the mode of the illumination lightirradiated on the face to be verified.

For example, the image acquisition device 120 can be configured toacquire a plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified by recordingvideos or capturing (for example, continuously capturing) a plurality ofimages. For example, in a case of adopting the method of allowing theimage acquisition device 120 to continuously capture the plurality ofimages, the image acquisition device 120 can be synchronized with thedisplay device, so as to acquire the plurality of illumination images,which are respectively corresponding to various modes, of the face to beverified by means of capturing the plurality of images. For example,identification information such as time can be attached to each capturedimage so as to realize time alignment in a subsequent stage forverification processing. For example, a time length of the recordedvideo or a number of frames of the captured image can be set accordingto specific implementation demands. No limitation will be given herein.

For example, the image acquisition device 120 can be implemented intovarious types. For example, the image acquisition device 120 can includeat least one camera, and the camera can include at least one selectedfrom a complementary metal oxide semiconductor (CMOS) image sensor, acharge-coupled device (CCD) image sensor, and the like. For example,when the image acquisition device 120 includes two cameras, the twocameras can be the same or can also be different. For example, in a casethat two cameras of the image acquisition device 120 are different, onecamera can have a high resolution and the other camera can have a lowresolution, or one camera can be a full-color camera and the othercamera can be a black and white camera. The configurations of the imageacquisition devices in other embodiments of the present disclosure aresimilar to the above-mentioned configurations of the image acquisitiondevice 120.

Description will be given below with reference to FIGS. 6A and 6B toillustrative methods of adopting the image acquisition device 120 toacquire the plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified. For example,the face to be verified can be located at a light-emitting side of thedisplay screen 110; and the face to be verified can be a face of livinghuman 193, or a face image and/or video displayed by a second displayscreen 194 (namely a display screen used for malicious attack). FIGS. 6Aand 6B respectively show illustrative methods of adopting the imageacquisition device 120 to acquire the plurality of illumination images,which are respectively corresponding to various modes, of the face to beverified in a case that the face to be verified is the face of livinghuman 193 and the face image and/or video displayed by the seconddisplay screen 194.

For example, at least partially light emitted by the luminous area ofthe first area 151 is irradiated on the face to be verified; the face tobe verified at least partially reflects the illumination lightirradiated on the face to be verified; and at least partially lightreflected by the face to be verified is acquired by the imageacquisition device 120. For example, the original video and/or imageacquired by the image acquisition device 120 can be denoted as A[i], andi represents the numeral of images.

For example, FIG. 6A illustrates an illustrative scene of adopting theimage acquisition device to acquire the plurality of illuminationimages, which are respectively corresponding to various modes, of theface to be verified in an embodiment of the present disclosure (namelythe scene where the face to be verified is the face of living human193). As illustrated in FIG. 6A, when the face to be verified is theface of living human 193, light acquired by the image acquisition device120 comprises reflected light 191 of the face to be verified (i.e., thelight reflected by the face to be verified) and ambient light (not shownin FIG. 6A). When the mode (the color and/or the position) of theillumination light irradiated on the face of living human 193 changes,the images, which are corresponding to various modes of the illuminationlight respectively and acquired by the image acquisition device 120, ofthe face of living human 193 can change correspondingly according to thechange of the modes of the illumination light.

For example, FIG. 6B illustrates another illustrative scene of adoptingthe image acquisition device to acquire the plurality of illuminationimages, which are respectively corresponding to various modes, of theface to be verified in an embodiment of the present disclosure (namelythe scene where the face to be verified is a face image and/or videodisplayed by the second display screen 194). As illustrated in FIG. 6B,when the face to be verified is the face image and/or video displayed bythe second display screen 194, light acquired by the image acquisitiondevice 120 comprises the reflected light 191 of the face to be verified,self-luminous light 192 of the second display screen 194, and ambientlight (not shown in FIG. 6B). In the above-mentioned three kinds oflight, only the self-luminous light 192 of the second display screen 194can be used for forming a face image by the image acquisition device120. The light reflected by the second display screen 194 (that is, thereflected light 191 of the face to be verified displayed by the seconddisplay screen 194) generally include specular reflection component andcannot be used for forming a face image by the image acquisition device120; furthermore, the reflected light of the face to be verifieddisplayed by the second display screen (i.e., the light reflected by thesecond display screen) exist in the form of the noise of the face imageformed by the image acquisition device 120. Therefore, in a case ofchanging the mode (the color and/or the position) of the illuminationlight irradiated on the face of living human, even in a case that thelight reflected by the second display screen 194 (that is, the reflectedlight 191 of the face to be verified displayed by the second displayscreen) are slightly changed, a light reflection characteristic of theface to be verified displayed by the second display screen 194 can besubstantially unaffected. Thus, the face of living human 193 and theface image and/or video displayed by the second display screen 194 canbe distinguished according to the light reflection characteristic of theface to be verified. That is to say, the illumination liveness detectionresult can be obtained according to the light reflection characteristicof the plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified acquired bythe image acquisition device 120, and therefore, whether or not the faceto be verified is a face of living human can be determined at leastaccording to the illumination liveness detection result.

For example, as illustrated in FIG. 1A, the processing device 130 caninclude a processor and a memory. The processor is, for example, acentral processor (CPU), a microprocessor or a processor in other formshaving data processing capability and/or instruction executioncapability, and can be implemented by an X86 architecture or an ARMarchitecture. For example, the processor can be a general purposeprocessor and can also be a microcontroller, a microprocessor, a digitalsignal processor (DSP), a dedicated image processing chip, a fieldprogrammable logic array, etc. The processing devices in the followingembodiments are similar to that in the embodiment. The memory, forexample, can include a volatile memory and/or a nonvolatile memory, forexample, can include various types of storages or storage media such asa read-only memory (ROM), a hard disc or a flash memory.Correspondingly, the memory can be embodied as one or more computerprogram products; the computer program products can include computerreadable storage media in various forms; and one or more computerprogram instructions can be stored on the computer readable storagemedium. The processor can run the program instructions, so as to realizethe following functions and/or other expected functions of the processorin the embodiment of the present disclosure. For example, theillumination liveness detection result can be obtained according to thelight reflection characteristic of the face to be verified in theplurality of illumination images, and hence whether or not the face tobe verified is a face of living human can be determined at leastaccording to the illumination liveness detection result. The memory canalso store various other applications (APPs) and data, for example, theplurality of illumination images, which are respectively correspondingto various modes, of the face to be verified acquired by the imageacquisition device, and various data used and/or produced by the APPs.

For example, the processing device 130 can be configured to analyze theplurality of illumination images, to acquire the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images, and hence to obtain the illumination livenessdetection result. The processing device 130 can be further configured todetermine whether or not the face to be verified passes the faceliveness detection at least according to the illumination livenessdetection result. That is to say, whether or not the face to be verifiedis a face of living human can be determined at least according to thelight reflection characteristic of the face to be verified in theplurality of illumination images.

For example, the processing device 130 can determine whether or not theface to be verified is a face of living human only according to theillumination liveness detection result. For another example, theprocessing device 130 can also determine whether or not the face to beverified is a face of living human according to the illuminationliveness detection result and the action liveness detection result. Forclarity, description will be only given below to an example of adoptingthe processing device 130 to determine whether or not the face to beverified is a face of living human according to the illuminationliveness detection result, and an example of determining whether or notthe face to be verified is a face of living human according to theillumination liveness detection result and the action liveness detectionresult will be described later.

For example, the processing device 130 can be configured to select sameone part of the face to be verified (for example, the cheek of the faceto be verified) in the plurality of illumination images, calculate thelight reflection characteristics of the one part of the face to beverified in the plurality of illumination images, and obtain theillumination liveness detection result (i.e., the result regardingwhether or not the face to be verified passes the illumination livenessdetection) according to the light reflection characteristics of the onepart of the face to be verified in the plurality of illumination images.For another example, the processing device 130 can be further configuredto select parts located at same one position of the plurality ofillumination images (for example, at the center position of theplurality of illumination images), calculate light reflectioncharacteristics of the parts located at the one position of theplurality of illumination images, and obtain the illumination livenessdetection result (i.e., the result regarding whether or not the face tobe verified passes the illumination liveness detection) according to thelight reflection characteristics of the parts located at the oneposition of the plurality of illumination images, and thus whether ornot the face to be verified is a face of living human can be determined.For example, the method of adopting the processing device 130 todetermine whether or not the face to be verified is a face of livinghuman can be selected according to specific implementation demands. Nospecific limitation will be given herein the embodiment of the presentdisclosure.

For example, the processing device 130 can determine whether or not theface to be verified passes the illumination liveness detection accordingto the light reflection characteristic of the face to be verified in theplurality of illumination images with reference to the following stepsas illustrated in FIG. 7.

S110: image preprocessing;

S120: information extraction;

S130: face liveness determination.

For example, the objective of the image preprocessing in the step S110is to ensure the consistency of the size, the position and the qualityof a target object. For example, according to specific implementationdemands, the target object can be the entire face region of the face tobe verified or partial area of the face to be verified. No specificlimitation will be given here.

For another example, displacement can be occurred for the face to beverified in the process of recording the video or capturing theplurality of images, and the positions of the target object, in theplurality of face images, in each image can be consistent by means ofalignment compensation. The consistency herein refers to that thepositions of the target object in the plurality of frames of faceimages, in each image are substantially same (for example, the maximumvalue of the position deviation of the target object is less than apredetermined value), not requiring to have exactly same position.

For example, the target object can be aligned by the following alignmentcompensation algorithm. (1), a positioning image B[i] and a signal imageC[i] are calculated according to an original video and/or image A[i].For example, firstly, (r2−r1, g2−g1, b2−b1) can be normalized to a unitvector (u, v, w); secondly, the color (r′, g′, b′) of pixels atcorresponding positions in the positioning image B[i] is calculatedaccording to the color (r, g, b) of each pixel in the original videoand/or image A[i], in which r′=r−(r×u+g×v+b×w)×u, g′=g−(r×u+g×v+b×w)×v,b′=b−(r×u+g×v+b×w)×w; and finally, the color k of pixels atcorresponding positions in the signal image C[i] is calculated,k=r×u+g×v+b×w. (2), the face region is extracted in B[0]. (3), a tracingalgorithm (e.g., Lucas Kanade tracing algorithm) is applied to B[1] andlater images (namely the first image and later images); face regions inB[1] and later images are aligned with a face region in B[0]; and analignment parameter of each image is recorded. (4), the signal imageC[i] is aligned by adoption of the same group of alignment parameters,and the value C[i, x, y] at the (x, y) position of the i^(th) image inthe aligned signal image C is acquired.

For example, although the step S110 can improve the efficiency and thereliability of determining whether or not the face to be verified passesthe illumination liveness detection, the step S110 is not anindispensable step and can be adopted according to specificimplementation demands. No specific limitation will be given here in theembodiment of the present disclosure.

For example, in order to extract the change information (signal) of theimage of the target object, due to the change of the color and/or theposition of the pattern displayed by the display screen 110, from thevideo or the plurality of images acquired by the image acquisitiondevice 120, the information (signal) extraction in the step S120 can beperformed. For example, the method of extracting change information(signal) of the image of the target object due to the change of thecolor and/or the position of the pattern displayed by the display screen110 can be selected according to specific implementation demands. Nolimitation will be given here in the embodiment of the presentdisclosure. For example, information extraction can be realized by amethod of calculating a correlation image S[x,y] (namely calculating thecorrelation between the plurality of illumination images andcorresponding modes of the illumination light). For example, thecorrelation image can be calculated according to the aligned signalimage C[i, x, y] and the real number sequence P[i],S[x,y]=sum(C[i,x,y]*P[i]). For example, the physical meaning of thecorrelation image is as follows: being a face image when the face to beverified is a face of living human; and being unable to generatecorresponding face image when the face to be verified is the face imageand/or video displayed by the second display screen 194. Signals (e.g.,ambient light) which do not correlated with the mode of the illuminationlight irradiated on the face to be verified can be filtered out bycalculation of the correlation between the aligned signal image C[i, x,y] and the real number sequence P[i]. In addition, if the real numbersequence P[i] is generated according to the unique identifier in realtime, the attacker cannot immediately generate a face animation sequencecorresponding to the unique identifier, so the safety of the device forface liveness detection can be further improved.

For example, in order to ensure the reliability of the information(signal) extracted in the step S120 and the face liveness determinationresult, the reliability of the extracted signal can also be calculatedbefore the step S130 (i.e., face liveness determination) and after thestep of obtaining the correlation image S[x,y]. For example, thereliability of the extracted signal can be calculated by the followingmeans.

(1) Calculating k1=sum(S[x,y]̂2) according to the correlation imageS[x,y].

(2) Calculating k2=k1/sum_{x,y} variance_i(C[i,x,y]) according to k1 andthe aligned signal image C[i, x, y].

(3) Determining the reliability of the extracted signal according topreset thresholds and the values k1 and k2 obtained by calculation. Forexample, when the values k1 and k2 do not reach the preset thresholds(for example, can indicate that the image acquisition device 120 isilluminated by bright light other than the illumination light duringacquiring the plurality of illumination images), the user can beprompted to change position (for example, finding a place without thebright light) and perform the face liveness detection again.

For example, the objective of the face liveness determination in thestep S130 is to determine whether or not the change of the image of thetarget object extracted in the step S120 is in line with the change ruleof a face of living human, and hence the illumination liveness detectionresult can be obtained. For example, the above-mentioned face livenessdetermination process can be realized by means of machine learning. Forexample, change information of the image of the target object extractedin the step S120 (e.g., the correlation image S[x,y] of the targetobject) can be inputted into a pre-trained classifier, and theclassifier is adopted to determine whether or not the image, which iscorresponding to the change information of the image of the targetobject (i.e., the correlation image S[x,y]), is an image obtained in theprocess of capturing photos of the face of living human (for example,obtained in the process of capturing photos of a three dimensionalface), that is, the pre-trained classifier is adopted to determinewhether or not the face to be verified passes the illumination livenessdetection, and thus whether or not the face to be verified is the faceof living human can be determined.

For example, the classifier can be a convolution neural network, asupport vector machine or other applicable classifiers. No specificlimitation will be given here in the embodiment of the presentdisclosure. For example, the classifier can be trained by the followingmethod: firstly, adopting the device for face liveness detection 100provided by the embodiment to acquire videos or a plurality of imagesfrom a large number of face of living humans and face images and/orvideos displayed by the second display screen 194 during dynamicallychanging the mode of the illumination light irradiated on the face ofliving humans and the face images and/or videos displayed by the seconddisplay screen 194; secondly, acquiring a correlation image S[x,y] ofthe acquired face videos or the plurality of images through, forexample, the above-mentioned method; and finally, the correlation imageS[x,y] and information regarding whether or not corresponding image isan image obtained in the process of capturing the photos of the face ofliving human (for example, obtained in the process of capturing photosof a three dimensional face) are inputted into the classifier, and thenthe training of the classifier can be realized.

For example, the processing device 130 can also directly obtain theillumination liveness detection result by means of machine learningaccording to the light reflection characteristic of the face to beverified in the plurality of illumination images, such that whether ornot the face to be verified is a face of living human can be determined.For example, the videos or the plurality of images acquired by the imageacquisition device 120 and the unique identifier (or the real numbersequence P[i]) can be inputted into the pre-trained classifier (e.g., arecurrent neural network (RNN)), and the classifier is adopted todetermine whether or not the image is an image obtained in the processof capturing the photos of the face of living human (for example,obtained in the process of capturing photos of a three dimensionalface).

For example, FIG. 8A is an illustrative block diagram of another devicefor face liveness detection 200 provided by an embodiment of the presentdisclosure, and FIG. 8B is a schematic diagram of the device for faceliveness detection 200 as illustrated in FIG. 8A. As illustrated inFIGS. 8A and 8B, the device for face liveness detection 200 comprises aprojecting device 210 (e.g., a micro-projector), an image acquisitiondevice 220 and a processing device 230. For example, the imageacquisition device 220 and the projecting device 210 can be disposed atsame side of the device for face liveness detection 200 (for example, alight-emitting side of the device for face liveness detection 200). Forexample, the device for face liveness detection 200 further comprises adisplay screen. The display screen can be configured to display relevantinformation helping the user to use the device for face livenessdetection 200. For example, the image acquisition device 220 can bedisposed at a periphery region (e.g., an upper side region) of thedisplay screen. For example, the projecting device 210 can be disposedat a periphery region (e.g., an upper side region) of the displayscreen. For example, the processing device 230 can be disposed at anopposite side of the light-emitting side of the display screen. Thedevice for face liveness detection 200 is, for example, can be embodiedin various forms; for example, the device for face liveness detection200 can be a special purpose device intended for the face livenessdetection; for another example, the device for face liveness detection200 can also be used as a component of an entrance guard system or acomponent of equipment such as an ATM; for further another example, thedevice for face liveness detection 200 can also be a component of otherelectronic devices (e.g., a mobile phone, a tablet PC, a notebookcomputer and smart glasses).

For example, in the embodiment, a light source in the projecting device210 can be used as the light source of the device for face livenessdetection 200. For example, the projecting device 210 can be configuredto dynamically change the mode of illumination light irradiated on theface to be verified. For example, the position of the illumination lightcan be dynamically changed by changing a relative position between aprojection region of the projecting device 210 and the face to beverified (namely changing an angle between light emitted by the lightsource of the projecting device 210 and the projecting device 210), andthe projection region can be located at any position of the face to beverified according to specific implementation demands. For anotherexample, the color of the illumination light can also be dynamicallychanged by changing the color of the emitted light of the light sourceof the projecting device 210. For further another example, the mode ofthe light emitted by the projecting device 210 can also be dynamicallychanged by changing the contents displayed by the projecting device 210(namely the position of the projection region and/or the color presentedby the projection region), and hence the mode of the illumination lightirradiated on the face to be verified can be dynamically changed. Forexample, the method for changing the position of the projection regionand/or the color presented by the projection region of the projectingdevice 210 can refer to relevant content of the projection displaytechnology. No further description will be given herein.

For example, the image acquisition device 220 can be configured toacquire a plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified. For example,the processing device 230 can be configured to determine whether or notthe face to be verified passes the illumination liveness detectionaccording to the light reflection characteristic of the face to beverified in the plurality of illumination images, such that whether ornot the face to be verified is a face of living human can be determined.For example, the relevant content of the image acquisition device 220and the processing device 230 can refer to the embodiment of the devicefor face liveness detection, which is illustrated in FIG. 1A. No furtherdescription will be given herein. For example, face liveness detectioncan be realized by the device for face liveness detection 200 and hencethe safety of the device for face liveness detection can be improved.

For example, FIG. 9 is an illustrative block diagram of further anotherdevice for face liveness detection 300 provided by an embodiment of thepresent disclosure. As illustrated in FIG. 9, the device for faceliveness detection 300 can comprise a light source 310, an imageacquisition device 320 and a processing device 330. For example, thedevice for face liveness detection 300 can be a special purpose deviceintended for the face liveness detection; for another example, thedevice for face liveness detection 300 can also be used as a componentof an entrance guard system or a component of equipment such as an ATM;for further another example, the device for face liveness detection 300can also be a component of other electronic devices (e.g., a mobilephone, a tablet PC or a notebook computer).

For example, the light source 310 includes a luminous component 311 anda reflection component 312. The light source 310 can be configured todynamically change the mode of illumination light irradiated on the faceto be verified. The type, the emission wavelength (namely the color) andthe number of the luminous components of the light source 310 can beselected according to specific implementation demands. No specificlimitation will be given here in the embodiment of the presentdisclosure.

For example, the light source 310 can include red, green and blue LEDs.For example, the color of the emitted light of the light source 310 canbe controlled by control of the luminous intensity of the red, green andblue LEDs, and hence the color and the mode of the illumination lightirradiated on the face to be verified can be dynamically changed.

For example, the reflection component 312 can be configured to changethe direction of emitted light of the luminous component 311 relative tothe device for face liveness detection 300, and hence can dynamicallychange the position and the mode of the illumination light irradiated onthe face to be verified. For example, the type of the reflectioncomponent 312 can be set according to specific implementation demands.No specific limitation will be given here in the embodiment of thepresent disclosure. For example, the reflection component 312 can be aspatial light modulator (e.g., a digital micro device (DMD) or a liquidcrystal light valve) or a reflective mirror having angle adjustmentfunction.

For example, the image acquisition device 320 can be configured toacquire a plurality of illumination images corresponding to variousmodes respectively. For example, the processing device 330 can beconfigured to determine whether or not the face to be verified passesthe illumination liveness detection according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images, such that whether or not the face to be verified isa face of living human can be determined. For example, theconfigurations of the image acquisition device 320 and the processingdevice 330 can refer to the embodiment of the device for face livenessdetection, which is illustrated in FIG. 1A. No further description willbe given herein. For example, face liveness detection can be realized bythe device for face liveness detection 300 and hence the safety of thedevice for face liveness detection can be improved.

For example, FIG. 10 is an illustrative block diagram of still anotherdevice for face liveness detection 400 provided by an embodiment of thepresent disclosure. As illustrated in FIG. 10, the device for faceliveness detection 400 can comprise a light source 410, an imageacquisition device 420 and a processing device 430. For example, thedevice for face liveness detection 400 can be a special purpose deviceintended for the face liveness detection; for another example, thedevice for face liveness detection 400 can also be used as a componentof an entrance guard system or a component of equipment such as an ATM.

For example, the light source 410 includes a luminous component 411 anda translation component 412. The luminous component 411 is provided onthe translation component 412. For example, the translation component412 can be configured to change the position of the luminous component411, and hence can dynamically change the position and the mode ofillumination light irradiated on the face to be verified. For example,the type of the translation component 412 can be set according tospecific implementation demands. No specific limitation will be givenhere in the embodiment of the present disclosure. For example, thetranslation component 412 can be implemented by a one-dimensionalelectric translation stage, a two-dimensional electric translationstage, a guide rail and the like, and for example, the translationcomponent 412 can be driven by a stepping motor.

The relevant content regarding the light source 410, the imageacquisition device 420 and the processing device 430 can refer to theembodiment of the device for face liveness detection, which isillustrated in FIG. 1A. No further description will be given herein. Forexample, face liveness detection can be realized by the device for faceliveness detection 400 and hence the safety of the device for faceliveness detection can be improved.

For example, FIG. 11 is an illustrative block diagram of a device forface liveness detection 500 provided by another embodiment of thepresent disclosure. As illustrated in FIG. 11, the device for faceliveness detection 500 can comprise a light source 510, an imageacquisition device 520, an information transmitter and receiver 530 anda processing device 540. For example, the device for face livenessdetection 500 can be a component of other electronic devices (e.g., amobile phone, a tablet PC or a notebook computer). For another example,the device for face liveness detection 500 can also be a special purposedevice intended for the face liveness detection. For further anotherexample, the device for face liveness detection 500 can also be used asa component of an entrance guard system or a component of equipment suchas an ATM.

For example, the relevant content regarding the light source 510 and theimage acquisition device 520 can refer to the embodiment of the devicefor face liveness detection illustrated in FIG. 1A. No furtherdescription will be given herein.

For example, the information transmitter and receiver 530 can beconfigured to send a plurality of illumination images of the face to beverified to a server, and then receive at least one of the followinginformation from the server: the illumination liveness detection resultdetermined according to the light reflection characteristic of the faceto be verified in the plurality of illumination images; determinationinformation, which is obtained at least according to the illuminationliveness detection result, regarding whether or not the face to beverified is a face of living human. For example, the informationtransmitter and receiver 530 can send the plurality of illuminationimages of the face to be verified to the server via network or othertechnologies, and receive the determination information regardingwhether or not the face to be verified is a face of living human (and/orthe illumination liveness detection result) via network or othertechnologies. For example, the network can be Internet, wireless localarea network (WLAN), mobile communication network and the like; forexample, the other technologies can include Bluetooth communicationtechnology, infrared communication technology, etc. For example, theserver can be a general purpose server or a special purpose server andcan be a virtual server, a cloud server, etc. For example, theinformation transmitter and receiver 530 can include a modem, a networkadapter, a Bluetooth transceiver, an infrared transceiver and the like,and for example, the information transmitter and receiver 530 can alsoperform operations such as coding and decoding of the sent informationor the received information.

For example, the processing device 540 can be configured to determinewhether or not the face to be verified passes the face livenessdetection (i.e., face liveness authentication) on the basis of thedetermination information regarding whether or not the face to beverified is a face of living human (and/or the illumination livenessdetection result). Because the process of determining whether or not theface to be verified is a face of living human at least according to thelight reflection characteristic of the face to be verified in theplurality of illumination images is performed by the server,computational resources of the processing device 540 can be saved. Thus,the requirements on the performances of the processing device 540 andthe production cost of the device for face liveness detection 500 can bereduced, and therefore the user experience can be improved. For example,face liveness detection can be realized by the device for face livenessdetection 500 and hence the safety of the device for face livenessdetection 500 can be improved.

For example, FIG. 12 is an illustrative block diagram of a device forface liveness detection 600 provided by further another embodiment ofthe present disclosure. As illustrated in FIG. 12, the device for faceliveness detection 600 can comprise an information transmitter andreceiver 610 and a processing device 620. For example, the function ofthe device for face liveness detection 600 can be realized by a server.The server can be a general purpose server or a special purpose serverand can be a virtual server, a cloud server, etc. The informationtransmitter and receiver 610 can include a modem, a network adapter, aBluetooth transceiver, an infrared transceiver and the like; forexample, the information transmitter and receiver 610 can alsoperforming operations such as coding and decoding of the sentinformation or the received information.

For example, the information transmitter and receiver 610 can beconfigured to receive a plurality of illumination images of the face tobe verified from a client. For example, the processing device 620 can beconfigured to obtain the illumination liveness detection resultaccording to the light reflection characteristic of the face to beverified in the plurality of illumination images; for example, theprocessing device 620 can be further configured to obtain determinationinformation, which is obtained at least according to the illuminationliveness detection result, regarding whether or not the face to beverified is a face of living human. For example, the informationtransmitter and receiver 610 can be further configured to senddetermination information regarding whether or not the face to beverified is a face of living human (and/or an illumination livenessdetection result) to the client. The relevant content regarding theprocessing device 620 can refer to the embodiment of the device for faceliveness detection illustrated in FIG. 1A. No further description willbe given herein. For example, face liveness detection can be realized bythe device for face liveness detection 600 and hence the safety of thedevice for face liveness detection 600 can be improved.

For example, the device for face liveness detection 500 as illustratedin FIG. 11 can be used as the client, the device for face livenessdetection 600 as illustrated in FIG. 12 can be used as the server, andthe device for face liveness detection 500 and the device for faceliveness detection 600 can be cooperated with each other to form a faceliveness detection system. The client and the server can be arranged atthe same place or different places.

For example, FIG. 13 is an illustrative block diagram of a device forface liveness detection 700 provided by still another embodiment of thepresent disclosure. As illustrated in FIG. 13, the device for faceliveness detection 700 can comprise a processing device 710, a memory720, and computer program instructions are stored in the memory 720. Thecomputer program instructions execute the following steps when run bythe processing device 710.

S710: acquiring a plurality of illumination images of a face to beverified, wherein the plurality of illumination images are captured in aprocess of dynamically changing mode of illumination light irradiated onthe face to be verified and are respectively corresponding to variousmodes of the illumination light.

S720: obtaining an illumination liveness detection result according to alight reflection characteristic of the face to be verified in theplurality of illumination images; and

S730: determining whether or not the face to be verified passes the faceliveness detection at least according to the illumination livenessdetection result.

For example, according to specific implementation demands, the devicefor face liveness detection 700 can further comprise a light source andan image acquisition device. For example, the light source is configuredto dynamically change the mode of the illumination light irradiated onthe face to be verified; and the image acquisition device is configuredto acquire the plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified. For example,the light source and the image acquisition device can be adopted toacquire the plurality of illumination images, which are respectivelycorresponding to various modes, of the face to be verified captured inthe process of dynamically changing the mode of the illumination lightirradiated on the face to be verified.

For example, the processing device 710 can be a CPU or a processor inother forms having data processing capability and/or instructionexecution capability, and can be implemented by an X86 architecture oran ARM architecture. For example, the processing device 710 can be ageneral purpose processor and can also be a microcontroller, amicroprocessor, a DSP, a dedicated image processing chip, a fieldprogrammable logic array, etc.

For example, the memory 720, for example, can include a volatile memoryand/or a nonvolatile memory, for example, can include various types ofstorages or storage media such as an ROM, a hard disc and a flashmemory. Correspondingly, the memory 720 can be embodied as one or morecomputer program products; the computer program products can includevarious forms of computer readable storage media; and one or morecomputer program instructions can be stored on the computer readablestorage medium.

For example, the processing device 710 can run the program instructions,so as to realize the following functions and/or other expected functionsof the device for face liveness detection in the embodiment of thepresent disclosure, for example, can determine whether or not the faceto be verified passes the illumination liveness detection according tothe light reflection characteristic of the face to be verified in theplurality of illumination images.

For example, the memory 720 can also store various other applications(APPs) and various data, for example, the plurality of illuminationimages, which are respectively corresponding to various modes, of theface to be verified acquired by the image acquisition device, andvarious data used and/or produced by the APPs.

For example, in the step S710, the mode of the illumination lightirradiated on the face to be verified can be dynamically changed bydynamically changing the color and/or position of the illumination light(for example, the color and/or position of the illumination light can bedynamically changed through controlling of the light source with theprocessing device 710).

For example, when the light source is at least partial area of thedisplay screen, the mode of light emitted by the display screen can bedynamically changed by changing the contents displayed on the displayscreen, and hence the mode of the illumination light irradiated on theface to be verified can be dynamically changed. For example, thecontents displayed on the display screen can be changed by displaying apredetermined pattern on the display screen and changing the colorand/or position of the predetermined pattern. For example, the positionof a luminous area, which is used as the light source, of the displayscreen can be changed along with time along the row direction, along thecolumn direction, or along the row direction and the column direction.Because the relative position between the luminous area of the displayscreen and the face to be verified can be changed, the region, which isirradiated by the light emitted by the luminous area, of the face to beverified can also be correspondingly changed, so the position of theillumination light can be dynamically changed, and hence the mode of theillumination light irradiated on the face to be verified can bedynamically changed.

For another example, when the light source is at least partial area ofthe display screen, the color of the luminous area, which is used as thelight source, of the display screen can change along with time, andhence the mode of the illumination light irradiated on the face to beverified can be dynamically changed. Obviously, the position and thecolor of the luminous area, used as the light source, of the displayscreen can be simultaneously changed along with time, and hence the modeof the illumination light irradiated on the face to be verified can bedynamically changed.

For example, when the light source of the projecting device is used asthe light source of the device for face liveness detection, the positionand/or color of the illumination light can be dynamically changed bychanging the relative position between the projection region of theprojecting device and the face to be verified, and/or by changing thecolor presented by projection region (or the light emitted by theluminous area), and hence the mode of the illumination light irradiatedon the face to be verified can be dynamically changed.

For example, when the light source includes special purpose luminouscomponents, the position of the illumination light irradiated on theface to be verified can be dynamically changed by the method of adoptingthe reflection component to change the direction of the light emitted bythe luminous component (for example, change the angle between the lightemitted by the luminous component and the device for face livenessdetection) and/or adopting the translation component to change theposition of the luminous component.

For example, when the special purpose luminous components include red,green and blue LEDs or laser diodes, the color of the light source canbe changed by changing the ratio of the luminous intensity of the red,green and blue LEDs, and hence the color of the illumination lightirradiated on the face to be verified can be dynamically changed. Forexample, the mode of the illumination light irradiated on the face to beverified can be dynamically changed by dynamically changing the positionand/or color of the illumination light irradiated on the face to beverified.

For example, the mode of the illumination light irradiated on the faceto be verified can be dynamically changed according to a predeterminedrule or randomly. For example, in a case of randomly and dynamicallychanging the mode of the illumination light irradiated on the face to beverified, at the initial moment of each the face liveness detectionprocess, a unique identifier corresponding to this face livenessdetection can be generated; subsequently, a real number sequencechanging along with time (i.e., a time-varying real number sequence) isgenerated according to the unique identifier; and then the mode of theillumination light irradiated on the face to be verified can bedynamically changed on the basis of the real time sequence. For example,the unique identifier can be generated in the device for face livenessdetection. For another example, the unique identifier can also begenerated on a device (e.g., a cloud server) separated from the devicefor face liveness detection and transmitted to the device for faceliveness detection. For example, the specific content of dynamicallychanging the mode of the illumination light irradiated on the face to beverified can be referred to the embodiment of the device for faceliveness detection illustrated in FIG IA. No further description will begiven herein.

For example, in the process of dynamically changing the mode of theillumination light irradiated on the face to be verified, the imageacquisition device, for example, can be adopted to acquire a pluralityof illumination images, which are respectively corresponding to variousmodes, of the face to be verified. For example, for each mode of theillumination light irradiated on the face to be verified, the imageacquisition device can capture at least one illumination image (forexample, capture one illumination image or three illumination images foreach mode of the illumination light). For example, the image acquisitiondevice can record a video including the plurality of illumination imagesof the face to be verified or can also continuously capture a pluralityof images.

For example, when the face to be verified is a face of living human,light acquired by the image acquisition device comprises reflected lightof the face to be verified (i.e., the light reflected by the face ofliving human) and ambient light; when the mode (the color and/orposition) of the illumination light irradiated on the face of livinghuman is changed, the images, which are corresponding to various modesof the illumination light respectively and acquired by the imageacquisition device, of the face of living human can be correspondinglychanged according to the change of the modes of the illumination light.

For example, when the face to be verified is a face image and/or videodisplayed by a second display screen (a display screen used formalicious attack), light acquired by the image acquisition devicecomprises reflected light of the face to be verified (i.e., the lightreflected by the second display screen), self-luminous light of thesecond display screen, and ambient light. Because the second displayscreen is a self-luminous object, among the three kinds of lightacquired by the image acquisition device, only the self-luminous lightof the second display screen can be used for forming a face image by theimage acquisition device. The reflected light of the face to be verifieddisplayed by the second display screen (i.e., the light reflected by thesecond display screen) generally includes specular reflection componentsand cannot be used for forming the face image by the image acquisitiondevice; furthermore, the reflected light of the face to be verifieddisplayed by the second display screen (i.e., the light reflected by thesecond display screen) exist in the form of the noise of the face imageformed by the image acquisition device. Thus, when the mode (the colorand/or position) of the illumination light irradiated on the face ofliving human changes, even in a case that the light reflected by thesecond display screen 194 (that is, the reflected light of the face tobe verified displayed by the second display screen) are slightlychanged, a light reflection characteristic of the face to be verifieddisplayed by the second display screen can be substantially unaffected.Therefore, the face of living human and the face image and/or videodisplayed by the second display screen can be distinguished according tothe light reflection characteristic of the face to be verified. That isto say, the illumination liveness detection result can be obtainedaccording to the light reflection characteristic of the plurality ofillumination images, which are respectively corresponding to variousmodes, of the face to be verified acquired by the image acquisitiondevice, and therefore, whether or not the face to be verified is a faceof living human can be determined.

For example, same one part of the face to be verified in the pluralityof illumination images can be selected; subsequently, the lightreflection characteristic of the one part of the face to be verified inthe plurality of illumination images can be calculated; and then whetheror not the face to be verified is a face of living human can bedetermined according to the light reflection characteristics of the onepart of the face to be verified in the plurality of illumination images.For another example, parts located at same one position of the pluralityof illumination images can also be selected; subsequently, the lightreflection characteristic of the parts located at the one position ofthe plurality of illumination images can be calculated; and then theillumination liveness detection result can be obtained according to thelight reflection characteristics of the parts located at the oneposition of the plurality of illumination images, thus whether or notthe face to be verified is a face of living human can be determined.

For example, obtaining of the illumination liveness detection resultaccording to the light reflection characteristic of the face to beverified in the plurality of illumination images in the step S720comprising the following steps.

S721: extracting information (signal) regarding the change of an imageof a target object, due to the change of the mode (e.g., the colorand/or position) of the illumination light irradiated on the face, fromthe acquired video or the plurality of images of the face to beverified; and

S722: obtaining the illumination liveness detection result according tothe extracted information (signal).

For example, the steps S721 and S722 can be implemented by theprocessing device 710. For example, data generated in the steps S721 andS722 can be stored in the memory 720.

For example, in the step S721, the method of extracting the changeinformation (signal) of the image of the target object due to the changeof the mode of the illumination light irradiated on the face to beverified can be selected according to specific implementation demands.No specific limitation will be given here in the embodiment of thepresent disclosure. For example, the change information of the image ofthe target object can be extracted by the method of calculating acorrelation image (for example, calculating the correlation between theplurality of illumination images and corresponding modes of theillumination light). For example, the specific method of calculating thecorrelation image can refer to the embodiment of the device for faceliveness detection illustrated in FIG. 1A. No further description willbe given herein.

For example, before the step S721, the plurality of illumination images,which are respectively corresponding to various modes, of the face to beverified acquired by the image acquisition device can be preprocessed.The objective of image preprocessing is to ensure the consistency of thetarget object in size, position and quality. For example, according tospecific implementation demands, the target object can be the entireface region of the face to be verified or partial area of the face to beverified. No specific limitation will be given here. For example, theposition of the target object, in the plurality of frames of faceimages, in each image can be consistent by means of alignmentcompensation. The consistency herein refers to that the positions of thetarget object in the plurality of frames of face images, in each imageare substantially same (for example, the maximum value of the positiondeviation of the target object is less than a predetermined value), notrequiring to have exactly same position. The specific method ofalignment compensation can refer to the embodiment of the device forface liveness detection illustrated in FIG. 1A. No further descriptionwill be given herein.

For example, after the step S721 and before the step S722, thereliability of the signal (e.g., the reliability of the correlationimage or the calculation result of the correlation) extracted in thestep S721 can also be calculated. For example, whether or not the faceto be verified is illuminated by bright light other than theillumination light during acquiring the plurality of illumination imagescan be determined by calculating the reliability of the extracted signalbased on the plurality of illumination images; and when there is brightlight other than the illumination light, the user is prompted to findinga place without the bright light to perform the face liveness detection.For example, the calculation and determination method of the reliabilityof the extracted signal can refer to the embodiment of the device forface liveness detection illustrated in FIG. 1A. No further descriptionwill be given herein.

For example, in the step S722, whether or not the change of the image ofthe target object is in line with the change rule of a face of livinghuman (for example, the change rule of a three dimensional face) can bedetermined according to the extracted signal (e.g., the correlationimage) by means of machine learning. For example, the above-mentionedface liveness determination process can be implemented by means ofmachine learning, so the illumination liveness detection result can beobtained, and hence the face liveness detection can be implemented. Forexample, the extracted signal about the change of the image of thetarget object can be inputted into a pre-trained classifier, and theclassifier is adopted to determine whether or not the image, which iscorresponding to the extracted signal, is an image obtained in theprocess of capturing photos of the face of living human (for example,capturing photos of a three dimensional face), that is, the pre-trainedclassifier is adopted to determine whether or not the face to beverified is a face of living human (for example, the three dimensionalface). For example, the classifier can be a convolution neural network,a support vector machine or other applicable classifiers. No specificlimitation will be given here in the embodiment of the presentdisclosure. For example, the method of training the classifier can referto the embodiment of the device for face liveness detection illustratedin FIG. 1A. No further description will be given herein.

For example, the method of machine learning can also be directly adoptedto determine whether or not the face to be verified is the face ofliving human (for example, the three dimensional face) according to thelight reflection characteristic of the face to be verified in theplurality of illumination images. For example, the video or theplurality of images acquired by, for example, the image acquisitiondevice and the unique identifier (or the real number sequence) can beinputted into the pre-trained classifier (e.g., an RNN), and theclassifier is adopted to determine whether or not the image is an imageobtained in the process of capturing the face of living human (forexample, the three dimensional face). Therefore, the device for faceliveness detection can realize the face liveness detection and hence canimprove the safety of the face liveness detection 700.

In an example of determining whether or not the face to be verifiedpasses the face liveness detection only according to the illuminationliveness detection result, if the illumination liveness detection resultindicates that the face to be verified fails to pass the illuminationliveness detection (for example, the face to be verified is not a threedimensional face), the face to be verified fails to pass the faceliveness detection; and if the illumination liveness detection resultindicates that the face to be verified passes the illumination livenessdetection, the face to be verified passes the face liveness detection.In an example of determining whether or not the face to be verifiedpasses the face liveness detection according to both of the illuminationliveness detection result and the action liveness detection result, ifthe illumination liveness detection result indicates that the face to beverified fails to pass the illumination liveness detection or the actionliveness detection result indicates that the face to be verified failsto pass the action liveness detection, the face to be verified fails topass face liveness detection; and if the illumination liveness detectionresult indicates that the face to be verified passes the illuminationliveness detection and the action liveness detection result alsoindicates that the face to be verified passes the action livenessdetection, the face to be verified passes the face liveness detection.

For example, FIG. 14 is an illustrative block diagram of a device forface liveness detection 800 provided by still another embodiment of thepresent disclosure. For example, compared with the device for faceliveness detection as illustrated in FIG. 1A, the device for faceliveness detection 800 provided by the embodiment not only comprises alight source 810, an image acquisition device 820 and a processingdevice 830 but also comprises an output device 840, in which the lightsource 810, the image acquisition device 820 and the processing device830 can be used for realizing the function of illumination livenessdetection. The specific content can refer to the above-mentionedrelevant content. No further description will be given herein.

For example, the image acquisition device 820, the processing device 830and the output device 840 can also be used for realizing the function ofaction liveness detection. More specifically, the output device 840 canbe configured to output an action instruction. For example, the actioninstruction is used for notifying the face to be verified to execute anaction corresponding to the action instruction. The image acquisitiondevice 820 can be further configured to acquire an action image of theface to be verified. The processing device 830 can be further configuredto acquire an action detection result by detecting the action executedby the face to be verified according to the action image, and obtain anaction liveness detection result according to the action detectionresult and the action instruction. For example, when the actiondetection result and the action instruction are matched with each otherin sequence, it indicates that the face to be verified passes the actionliveness detection. For example, the processing device 830 can befurther configured to determine whether or not the face to be verifiedpasses the face liveness detection according to both of the illuminationliveness detection result and the action liveness detection result.

The specific function and the implementation of the output device, theimage acquisition device and the processor for realizing the function ofaction liveness detection can refer to the embodiment of the method forface liveness detection described in the later portion of the presentdisclosure. No further description will be given herein.

In a case that whether or not the face to be verified passes the faceliveness detection is determined according to the illumination livenessdetection result and the action liveness detection result, the devicefor face liveness detection not only can effectively counteract themalicious attack by the videos and the images but also can effectivelycounteract mask attacks (e.g., counteract the malicious attacks of threedimensional human face mask). Therefore, the device for face livenessdetection provided by the embodiment can further refine the device andthe method for face liveness detection and further improve the safety ofthe device and method for face liveness detection.

According to specific implementation demands, the device for faceliveness detection provided by other embodiments of the presentdisclosure (for example, the device for face liveness detections asillustrated in FIGS. 8A, 9, 10, 11, 12 and 13) can be further configuredto realize the function of action liveness detection and to determinewhether or not face to be verified passes the face liveness detectionaccording to both of the illumination liveness detection result and theaction liveness detection result, so as to further improve the safety ofthe device for face liveness detection provided by the embodiment of thepresent disclosure.

For example, FIG. 15 is an illustrative block diagram of a device forface liveness detection 900 provided by still another embodiment of thepresent disclosure. As illustrated in FIG. 15, the device for faceliveness detection 900 can comprise a light source 910, an imageacquisition device 920, a processing device 930 and a conditiondetermination device 950. Optionally, the device for face livenessdetection 900 can further comprise an output device 940, so as torealize the function of action liveness detection together with theimage acquisition device 920 and the processing device 930.

For example, the condition determination device 950 can be configured todetermine whether or not a preset requirement of an image acquisitioncondition of the face to be verified is satisfied before the step ofacquiring illumination images, in which the image acquisition conditionat least comprises one or more selected from a position of the face tobe verified, a pose of the face to be verified and a size of the face tobe verified in a real-time image acquired by the image acquisitiondevice. The specific implementation of the condition determinationdevice 950 can refer to relevant description in the following embodimentof the method for face liveness detection. No further description willbe given herein.

For example, images (e.g., illumination images) with better quality canbe acquired by notifying the user to adjust the position and/or theorientation relative to the device for face liveness detection when thepreset requirement of an image acquisition condition of the face to beverified is not satisfied. Thus, not only the number of times needed forperforming the face liveness detection (e.g., the illumination livenessdetection) can be reduced but also the workload of image preprocessingcan be reduced, and therefore, the user experience can be improved.

FIG. 16 is a flow diagram of a method for face liveness detectionprovided by still another embodiment of the present disclosure. FIG. 16corresponds to the foregoing device for face liveness detection. Toavoid repetition, corresponding contents are appropriately omitted here.As illustrated in FIG. 16, the method for face liveness detectioncomprises the following steps.

S910: performing an illumination liveness detection and obtaining anillumination liveness detection result; and

S920: determining whether or not the face to be verified passes the faceliveness detection at least according to the illumination livenessdetection result.

For example, performing of the illumination liveness detection andobtaining of the illumination liveness detection result can include thefollowing step.

S10: acquiring a plurality of illumination images of the face to beverified, in which the plurality of illumination images are captured ina process of dynamically changing mode of illumination light irradiatedon the face to be verified and are respectively corresponding to variousmodes of the illumination light; and

S20: obtaining the illumination liveness detection result according to alight reflection characteristic of the face to be verified in theplurality of illumination images.

For example, acquiring of the plurality of illumination images of theface to be verified comprises: dynamically changing the mode ofillumination light irradiated on the face to be verified, and acquiringa plurality of illumination images corresponding to various modesrespectively.

For example, obtaining of the illumination liveness detection resultaccording to the light reflection characteristic of the face to beverified in the plurality of illumination images comprises: analyzingthe plurality of illumination images, acquiring the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images, and obtaining the illumination liveness detectionresult according to the light reflection characteristic.

The specific content regarding the steps S910, S920, S10 and S20 canrefer to the embodiment of the device for face liveness detectionillustrated in FIG. 1A. No further description will be given herein.

More specifically, the step of obtaining the illumination livenessdetection result according to the light reflection characteristic of theface to be verified in the plurality of illumination images comprisingthe following steps.

S201: extracting change information (signal) of an image of a targetobject, due to the change of the mode (e.g., the color and/or position)of the illumination light irradiated on the face to be verified, fromacquired videos or a plurality of images of the face to be verified; and

S202: obtaining the illumination liveness detection result according tothe extracted information (signal).

For example, after the step S201 and before the step S202, thereliability of the signal (e.g., a correlation image or a calculationresult of the correlation) extracted in the step S201 can also becalculated. The calculation and determination methods of the reliabilityof the extracted signal can refer to the embodiment of the device forface liveness detection illustrated in FIG. 1A. No further descriptionwill be given herein.

For example, in the step S202, whether or not the change of the image ofthe target object is in line with the change rule of a face of livinghuman (for example, the change rule of a three dimensional face) can bedetermined according to the extracted signal (e.g., the correlationimage) by means of machine learning. The specific content can refer tothe embodiment of the device for face liveness detection illustrated inFIG. 1A. No further description will be given herein.

Therefore, the face liveness detection can be realized by the method forface liveness detection and hence the safety of the method for faceliveness detection can be improved.

Description will be given below to a method for face liveness detectionprovided by the embodiment of the present disclosure with reference toFIG. 18. FIG. 18 is an illustrative flow diagram of a method for faceliveness detection SE200 provided by the embodiment of the presentdisclosure. The method for face liveness detection SE200 corresponds tothe foregoing embodiments of device for face liveness detection and is aspecific example of the foregoing method for face liveness detection, socorresponding contents are appropriately omitted here. It should beunderstood that the method for face liveness detection provided by theembodiment of the present disclosure is not limited to the exampleillustrated in FIG. 18.

For example, as illustrated in FIG. 18, the method for face livenessdetection SE200 comprises the following steps.

In step S210, acquiring one or more illumination images of a face to beverified irradiated by detection light.

For example, the face to be verified can be a face of a living human, aface of an object to be verified, a photo, a human face mask or a screenfor displaying the face.

Illustratively, a light source can be adopted to emit the detectionlight (e.g., illumination light) toward the face to be verified. Forexample, the light source can be controlled by a processor to emitlight. For example, other light-emitting devices can be used as thelight source; for another example, the light source can also be aspecial purpose light source; for further another example, the lightsource can also be a combination of a display screen and light sourcesof other types (e.g., the special purpose light source). The specificcontent can refer to the above-mentioned relevant content; no furtherdescription will be given herein.

For example, the mode of the detection light can include but not limitedto the color of the detection light, the intensity of the detectionlight, the illumination angle of the detection light, the wavelength ofthe detection light, and the frequency of the detection light. Forexample, in a case that at least part of the display screen is used asthe light source, the mode of the detection light can also include theposition of a luminous area of the display screen.

Illustratively, in the process of irradiating the face to be verified,the mode of the detection light can be unchanged, namely the lightsource can adopt the detection light with constant parameters (e.g.,color or wavelength is unchanged during an illumination livenessdetection) to irradiate the face to be verified. For example, in oneembodiment, the adopted light source can be a display screen of a mobileterminal; the color, the brightness and the like of each pixel can becontrolled, so the display screen can emit light with a specificpattern, e.g., structural light. In such a case, the specific color orbrightness displayed in a specific pixel region of the screen can be themode of the detection light selected by optimization based on a largenumber of experimental data. In a case that the face to be verified isirradiated by the detection light with constant parameters, the faceliveness detection can be rapidly and accurately executed by a specificalgorithm corresponding to this detection light with specific constantparameters, and an illumination liveness detection result regarding theface to be verified can be rapidly and accurately obtained. In such acase, one or more illumination images can be acquired under theirradiation of unchanged detection light, and then the illuminationliveness detection result of the face liveness detection can be obtainedon the basis of the illumination images.

For example, in the process of irradiating the face to be verified withthe detection light, the mode of the detection light can be changed atleast once. In such a case, the change frequency of the mode of thedetection light and the acquisition frequency of the images can have acorresponding relationship, so that at least one illumination image canbe acquired under the detection light of each mode. For example, theacquisition frequency of the images is an integer multiple of the changefrequency of the mode of the detection light. For example, in a casethat the change frequency of the mode of the detection light is 60 timesper minute, the acquisition frequency of the images can be 60 or 120times per minute, but the embodiment of the present application is notlimited to the case.

For example, the mode of the detection light changes between every twoconsecutive moments. The consecutive moments can be any specific timepoint in a predetermined period of time. For example, the mode of thedetection light can change once every one second. More information aboutlight reflection characteristic can be obtained by rapid change of themode of the detection light, so as to improve accuracy and efficiency ofthe face liveness detection according to the light reflectioncharacteristic.

For example, in the process of irradiating the face to be verified, themode of the detection light is randomly changed or preset.

In one example, the mode of the detection light is completely randomlychanged. For example, the adopted light source is a display screen of amobile terminal. For example, the color of each area of the displayscreen can be controlled; and as for each area, RGB value (i.e., colorvalues of red, green and red color) can be randomly selected within apredetermined RGB value range at each time of changing the mode of thedetection light and can be used as the color value of the area. Thedisplay screen can comprise a plurality of areas and the size of theareas can be randomly set. For example, each area can include one ormore pixels, and the size of two different areas can be same ordifferent.

In another example, the mode of the detection light can be preset. Forexample, the detection light can be set to irradiate the face to beverified for a total of 10 seconds; the change frequency of the mode ofthe detection light can be set to once per second; and the color, theposition, the intensity and the like of the detection light emitted eachtime can be preset. During the process of the face liveness detection,the light source can sequentially emit the detection light of tendifferent modes according to a preset manner. The preset modes of thedetection light can be comparatively effective modes for face livenessdetection obtained on the basis of previous experience, so the accuracyand the efficiency of the face liveness detection can be improved.

For example, the mode of the detection light irradiated on the face tobe verified can be dynamically changed by dynamically changing the colorof the detection light. For another example, the mode of the detectionlight irradiated on the face to be verified can also be dynamicallychanged by dynamically changing the position of the detection light(e.g., changing the position of the luminous area of the displayscreen). For further another example, the mode of the detection lightirradiated on the face to be verified can also be dynamically changed bysimultaneously and dynamically changing the color of the detection lightand the position of the detection light.

For example, the position of the detection light can be dynamicallychanged by changing the position of the light source, so that the region(e.g., cheek region of the face to be verified) of the face to beverified irradiated by the detection light can be changed. For anotherexample, the region of the face to be verified irradiated by thedetection light can also be dynamically changed by changing the angle ofemitted light of the light source.

For example, the adopted light source can be a display screen of amobile terminal, and the image acquisition device can be a camera (e.g.,a front camera) of the mobile terminal disposed on the same side withthe display screen. For example, compared with adopting an additionalspecial purpose light source, the present embodiment can adopt thedisplay screen and the camera of a conventional mobile terminal (such asa mobile phone) to realize the function of the light source and theimage acquisition device, therefore, the face liveness detection withthe above-mentioned configuration for example can better suit forapplication scenes such as remote account opening through a personalmobile terminal.

For example, the adopted mode of the light can comprise the color andthe position of the luminous area. For example, light of colors emittedat different positions of the display screen at the same time can bedifferent; for another example, light of the colors emitted at differentpositions of the display screen can be same at any time, while light ofcolors emitted by same one position of the display screen can bedifferent at different times. For example, compared with changing lightintensity of the light, the method of changing mode of the detectionlight through changing the color and the position of the luminous areanot only can have better detection effect, but also can reduce thestimulation of the light on the human eyes, and hence can improve theuser experience.

For example, when the face to be verified is irradiated by the detectionlight, the image acquisition device can be adopted to acquire images ofthe face to be verified irradiated by the detection light and obtain theillumination images. For example, the image acquisition device can becontrolled by the processor to acquire the images. For example, theimage acquisition device can send one or more acquired illuminationimages to a processor of the system or device for face livenessdetection. Illustratively, the number of the illumination imagesacquired under the irradiation of the light of each mode can be one ormore, and no limitation will be given here in the present disclosure. Itshould be understood by those skilled in the art that the face livenessdetection is mainly based on the face image. Therefore, according to theembodiment of the present disclosure, in the processes of acquiring theillumination images, action images and real-time images, the objectiveis to acquire images including the face be verified for face livenessdetection.

For example, the illumination images can be sent by a client device(e.g., a mobile terminal including a camera or a remote video tellermachine (VTM)) to the device for face liveness detection and thenprocessed by the processor of the device for face liveness detection.For another example, the illumination images can also be acquired by animage acquisition device (e.g., a camera) of the device for faceliveness detection and sent to the processor of the same device for faceliveness detection for relevant processing.

In step S220: determining whether or not the face to be verified passesthe illumination liveness detection (for example, whether or not theface to be verified is a three dimensional face) according to a lightreflection characteristic of the face to be verified in one or moreillumination images, and obtaining an illumination liveness detectionresult.

In step S230: determining whether or not the face to be verified passesthe face liveness detection at least according to the illuminationliveness detection result.

In one example, the illumination liveness detection result can bedirectly taken as the final face liveness detection result; in such acase, the face liveness detection method can have small calculationamount and high efficiency. In another example, whether or not the faceto be verified passes the face liveness detection can be determined bythe illumination liveness detection result in combination with resultobtained through other face liveness detection processes (namely whetheror not the face to be verified passes the face liveness detection can bedetermined by taking the illumination liveness detection result andother face liveness detection results obtained on the basis of otherface liveness detection processes into consideration), in such a case,the face liveness detection method can have high accuracy.

As described above, as the light reflection characteristic of the faceof living human (e.g., the three dimensional human face) is differentfrom the light reflection characteristic of an object such as a displayscreen or a photo, the real face of living human and a face displayed bythe screen or the face on the photo can be effectively distinguishedaccording to the light reflection characteristic. Therefore, the methodfor face liveness detection provided by the embodiment of the presentdisclosure can effectively counteract screen attackers or photoattackers, and hence can improve the safety and the user experience ofan authentication system or similar systems employing the method forface liveness detection.

Illustratively, the method for face liveness detection provided by theembodiment of the present disclosure can be implemented by a device, anapparatus or a system comprising a memory and a processor.

The method for face liveness detection provided by the embodiment of thepresent disclosure can be adopted by an image acquisition end, forexample, can be adopted by an image acquisition end of a financialsystem such as a bank management system or can be adopted by a mobileterminal such as a smart mobile phone and a tablet PC. Alternatively,the method for face liveness detection provided by the embodiment of thepresent disclosure can also be adopted by a server (e.g., a cloudserver) and a client. For example, light is emitted by the client andimages of the face to be verified are acquired by the client; theacquired images are transmitted to the server (e.g., a cloud server) bythe client; the illumination liveness detection result is obtained bythe server (e.g., a cloud server); and the illumination livenessdetection result (or a verification result, which is obtained (forexample, calculated) based on the illumination liveness detectionresult, regarding whether or not the face to be verified passes the faceliveness detection) is received by the client. Because the server canhave more powerful data processing capability than the client, when theillumination liveness detection result is obtained (for example,calculated) by the server, the verification speed (for example, faceliveness detection speed) can be improved and the user experience can beimproved. In addition, because the server can have higher processingspeed, a more complex algorithm for face liveness detection can beadopted, and therefore, obtaining of the illumination liveness detectionresult (or a verification result, which is obtained based on theillumination liveness detection result, regarding whether or not theface to be verified passes the face liveness detection) by the servercan improve the accuracy of face liveness detection.

For example, the attack methods adopted by the attackers can be various,although the face liveness detection on the basis of light reflectioncharacteristics can counteract screen attack or photo attack, the methodfor face liveness detection on the basis of light reflectioncharacteristic can fail to counteract some other attack methodsadopting, for example, a three dimensional human face mask. Therefore,in order to further refine the method for face liveness detection andimprove the safety of the method for the face liveness detection, otherface liveness detection processes (e.g., action liveness detection) canbe further incorporated into the method for face liveness detection onthe basis of the light reflection characteristic. One illustrativeembodiment will be described below.

FIG. 19 is an illustrative flow diagram of a method for face livenessdetection SE300 provided by the embodiment of the present disclosure.Steps S310, S320 and S330 of the method for face liveness detectionSE300 as illustrated in FIG. 19 are similar to the steps S210-S230 ofthe method for face liveness detection SE200 as illustrated in FIG. 18.The steps S310, S320 and S330 as illustrated in FIG. 19 can beunderstood by those skilled in the art with reference to relevantdescription in FIG. 18. No further description will be given herein.

For example, the method for face liveness detection SE300 provided bythe embodiment can further comprise the steps S340-S370, and byperforming the steps S340-S370, an action liveness detection result canbe obtained. For example, in the step S330, whether or not the face tobe verified passes the face liveness detection is determined accordingto the illumination liveness detection result and the action livenessdetection result.

For example, in the step S340, an action instruction used for notifyingthe face to be verified to execute an action corresponding to the actioninstruction is outputted.

Illustratively, the action instruction can be outputted by an outputdevice. For example, the output device can be a display screen, suchthat the output device can output text and/or picture promptinformation. For another example, the output device can also be aloudspeaker, such that the output device can output voice promptinformation. Illustratively, the action instruction can be outputtedrandomly or according to a predetermined rule. For example, the actioninstruction can include one instruction or an instruction sequenceformed by a series of action instructions. For example, the actioninstruction can prompt the face to be verified to execute one or moreaction selected from nod, shake the head, wink the eyes, open the mouth,etc.

For example, in the step S350: an action image of the face to beverified is acquired.

For example, the number of the acquired action image of the face to beverified can be set according to demands of specific implementations, nolimitations will be given here in the embodiments of the presentdisclosure. For example, the number of the acquired action images can beone times the number of action instructions; for another example, thenumber of the acquired action image can be five times the number ofaction instruction. The action image can be obtained by performing imageacquisition regarding the face to be verified during the actioninstruction is outputted or within a period of time after the actioninstruction is outputted. For example, the action image of the face tobe verified can be acquired during the action instruction (or the actioninstruction sequence) is outputted (for example, after one actioninstruction is outputted, acquiring at least one image of the face to beverified, and subsequently, outputting the next action instruction). Foranother example, the action image of the face to be verified can also beacquired after all the action instructions (or the action instructionsequence) are outputted. For example, acquiring the action image of theface to be verified can comprise: recording a video including the actionimage (for example, including a plurality of action images). For anotherexample, the action image can also be images or an image acquired by theimage acquisition device.

For example, in the step S360: the action executed by the face to beverified is detected on the basis of the action image, so as to obtainan action detection result.

Illustratively, face detection and key point recognition can beperformed for each action image, and the action executed by the face tobe verified can be determined according to the face contour and/or theface key points of the action image. For example, the action executed bythe face to be verified can be determined according to the changetendency of the face contour and/or the face key points of the pluralityof acquired action images, such that whether or not the action executedby the face to be verified is matched with the action instruction can bedetermined in subsequent step (for example, step S360). For anotherexample, the action executed by the face to be verified can also bedetermined by the face contour and/or the face key points of one actionimage.

For example, in the step S370: whether or not the face to be verifiedpasses the action liveness detection is determined according to theaction detection result and the action instruction, and an actionliveness detection result is obtained.

Illustratively, if the action executed by the face to be verified in theaction image is matched with the action instruction, the action livenessdetection result, which indicates that the face to be verified passesthe action liveness detection (namely action-based face livenessdetection), is obtained; and if the action executed by the face to beverified in the action image is not matched with the action instructionor if the action liveness detection result indicates that the face to beverified does not execute any action in the action image (namely noaction, executed by the face to be verified, is detected), the actionliveness detection result, which indicates that the face to be verifiedfails to pass the action liveness detection, is obtained, and it can befurther determined that the face to be verified does not belong to aliving body.

It should be understood that the method to determine whether or not theaction executed by the face to be verified in the action image ismatched with the action instruction can be set according to specificimplementations, no limitations will be given here in the embodiments ofthe present disclosure. For example, when the face to be verifiedexecutes a plurality of actions, and the plurality of actions executedby the face to be verified include the plurality of actions indicated bythe plurality of action instructions (for example, include all of theactions indicated by the plurality of action instructions), it isdetermined that the action executed by the face to be verified in theaction image is matched with the action instruction and the face to beverified passes the action liveness detection (i.e., it is determinedthat the face to be verified passes the action liveness detection in acase that the plurality of actions executed by the face to be verifiedinclude for example all of the actions indicated by the plurality ofaction instructions regardless of whether or not the sequence of theplurality of actions is consistent with the sequence of the plurality ofactions indicated by the plurality of action instructions). For anotherexample, when the plurality of actions executed by the face to beverified are the plurality of actions indicated by the plurality ofaction instructions, and the sequence of the plurality of actionsexecuted by the face to be verified is consistent with the sequence ofthe plurality of actions indicated by the action instructions, it isdetermined that the action executed by the face to be verified in theaction image is matched with the action instruction and the face to beverified passes the action liveness detection; that is to say, when theaction images are matched with the action instructions in sequence, itis determined that the face to be verified passes the action livenessdetection.

Illustratively, in the step S330, if the face to be verified passes theillumination liveness detection and the action liveness detection(namely both the illumination liveness detection result and the actionliveness detection result indicate that the face to be verified belongsto a living body), it is determined that the face to be verified passesthe face liveness detection; and if the face to be verified fails topass the illumination liveness detection or the action livenessdetection (namely if any of the illumination liveness detection resultand the action liveness detection result indicates that the face to beverified does not belong to a living body), it is determined that theface to be verified fails to pass the face liveness detection. It shouldbe understood that the above-mentioned methods are only illustrative,and there are other determination methods for determining whether or notthe face to be verified passes face liveness detection.

It should be noted that the execution sequence of the action livenessdetection (for example, the steps S340-S370) and the illuminationliveness detection (for example, the steps S310-S320) can be setaccording to specific implementation demands. No specific limitationwill be given here in the present disclosure. For example, theillumination liveness detection can be performed before performing theaction liveness detection; for another example, the illuminationliveness detection can also be performed after the action livenessdetection is performed.

The method for face liveness detection based on action livenessdetection can be independently implemented by an image acquisition end,for example, can be independently implemented by an image acquisitionend of a financial system such as a bank management system or a mobileterminal such as a smart mobile phone and a tablet PC. Alternatively,the method for face liveness detection based on action livenessdetection can also be implemented by a server (e.g., a cloud server) anda client together. For example, the action instruction can be generatedby the server or the client; the action images of the face to beverified are acquired by the client; the acquired action images are thentransmitted to the server (e.g., a cloud server) by the client; theaction liveness detection result is obtained by the server (e.g., acloud server); and then the action liveness detection result is receivedby the client from the server.

It should be noted that, in one example, the action liveness detectionand the illumination liveness detection can be combined and the combinedmethod can be a method for face liveness detection; in another example,the action liveness detection can also be an independent method for faceliveness detection. No specific limitation will be given here in theembodiment of the present disclosure.

For example, the method for face liveness detection based on actionliveness detection can effectively counteract the attack manners such asmask attack; For example, the method for face liveness detection, whichis combined with the action liveness detection and the illuminationliveness detection, can counteract various types of malicious attackseffectively, and hence the safety of the above-mentioned method for faceliveness detection and relevant authentication system adopted with themethod for face liveness detection can be further improved.

For example, obtaining of the action liveness detection result accordingto the action detection result and the action instruction comprises:determining that the face to be verified passes the action livenessdetection in a case that an action, which is executed by the face to beverified and matched with the action instruction, is detected in theaction image, which is acquired within a time period not greater than apreset time period of the action liveness detection, and determiningthat the face to be verified fails to passes the action livenessdetection in a case that the action, which is executed by the face to beverified and matched with the action instruction, is not detected in theaction image, which is acquired within the time period not greater thanthe preset time period of the action liveness detection.

For example, the action instruction (e.g., a text or voice instructionsuch as “Please Nod” or “Please Open Your Mouth”) can be randomlyoutputted to prompt the face to be verified to execute the actioncorresponding to the action instruction (e.g., nodding or opening themouth), and key points of a face region are detected to determine theaction executed by the face to be verified, and then whether or not theaction executed by the face to be verified is matched with the outputtedaction instruction can be determined. If the action, which is executedby the face to be verified and detected within the preset time period ofthe action liveness detection, is matched with the outputted actioninstruction, it is determined that the face to be verified passes theaction liveness detection; and if the action, which is executed by theface to be verified and detected within the preset time period of theaction liveness detection, is not matched with the outputted actioninstruction, or no action executed by the face to be verified isdetected within the preset time period of the action liveness detection,it can be determined that the face to be verified fails to pass theaction liveness detection. For example, the preset time period of theaction liveness detection is the time for reminding the face to beverified (or user) that the action corresponding to the actioninstruction is required to complete within the preset time period of theaction liveness detection. No matter whether or not the face to beverified completes executing the action corresponding to the actioninstruction within the time period of the preset time period of theaction liveness detection, the action liveness detection result isrecorded and the following step is subsequently executed.Illustratively, if the face to be verified does not complete executingthe action corresponding to the action instruction within the presettime period of the action liveness detection, it can be determined thatthe face to be verified fails to pass the action liveness detection.

For example, according to the embodiment of the present disclosure, themethod for face liveness detection SE300 can further comprise:increasing number of times for performing the action liveness detectionby one (for example, counting once with a counter) for executing thesteps S340-S370 each time (namely increasing number of times forperforming the action liveness detection by one for each performance ofthe action liveness detection), and so as to obtain the number of timesfor performing the action liveness detection.

For example, after the step S370 (namely after the step of obtaining theaction liveness detection result) and in a case that the action livenessdetection result indicates that the face to be verified fails to passthe action liveness detection, the method SE300 can further comprise:outputting first error information used for notifying a failure of theaction liveness detection; determining whether or not the number oftimes for performing the action liveness detection is greater than afirst counting threshold; and returning to the step S330 (namelydetermining whether or not the face to be verified passes the faceliveness detection according to both of the illumination livenessdetection result and the action liveness detection result) in a casethat the number of times for performing the action liveness detection isgreater than a first counting threshold, and returning to the step S340(namely executing the action liveness detection again) in a case thatthe number of times for performing the action liveness detection is notgreater than the first counting threshold, or returning to the step S310when the step S310 is executed before the step S340 and the number oftimes for performing the action liveness detection is not greater thanthe first counting threshold (namely performing the illuminationliveness detection again when the illumination liveness detection isperformed before the action liveness detection and the number of timesfor performing the action liveness detection is not greater than thefirst counting threshold).

Illustratively, a counter can be adopted and configured to count thenumber of times for performing the action liveness detection (forexample, the steps S340-S370). The counter can increase the number oftimes for performing the action liveness detection by one for eachperformance of the action liveness detection. For example, an outputresult of the counter can be the number of times for performing theaction liveness detection. For example, the counter can be reset afterthe entire method for face liveness detection (the method for faceliveness detection SE300) is ended.

For example, if the current action liveness detection result indicatesthat the face to be verified fails to pass the action livenessdetection, the first error information can be outputted. For example,the first error information can prompt the failure of the actionliveness detection. For example, if the number of times for performingthe action liveness detection is not greater than a first countingthreshold, the first error information can also prompt the face to beverified that a new face liveness detection process will be performed.For example, the new face liveness detection process can be the actionliveness detection. For another example, if the light face livenessdetection (for example, the steps S310-S320) are executed before theaction liveness detection, the new face liveness detection process canbe the illumination face liveness detection and the action livenessdetection; in such a case, the method for face liveness detection candirectly returned the step S310 (namely the illumination livenessdetection and the action liveness detection can be sequentially executedonce more), so as to improve the accuracy of the method for faceliveness detection.

For example, the first counting threshold can be any appropriate valueand can be set as required. No limitation will be given here in thepresent disclosure. For example, the first counting threshold can bethree; for another example, the first counting threshold can also befive.

For example, there can be various unexpected situations in actual faceliveness detection processes, for example, the user can fail to executethe action corresponding to the action instruction in time, the acquiredimage can be a blurred image, or the face detection result can be notaccurate enough. These unexpected situations can cause a face of aliving human fails to pass the face liveness detection. Therefore, inorder to balance the user experience and the safety of the method forthe face liveness detection, the frequency threshold can be set to allowthe user to have several chances (for example, three chances) to passthe action liveness detection. If the user fails to pass the actionliveness detection in a case that the number of times for performing theaction liveness detection is increased by the first counting threshold,it is can be determined that the face to be verified fails to pass theaction liveness detection, and thus it is can be determined that theface to be verified does not belong to a living body.

For example, according to the embodiment of the present disclosure,before the step S210 (or S310), the method for face liveness detectionSE200 (or SE300) can further comprise: S208: determining whether or nota preset requirement of an image acquisition condition of the face to beverified is satisfied, and executing the step S210 or S310 (namelyperforming the illumination liveness detection) if the presetrequirement of the image acquisition condition is satisfied, orperforming the action liveness detection if the preset requirement ofthe image acquisition condition is satisfied and the action livenessdetection is executed before the illumination liveness detection. Theimage acquisition conditions include the position of the face to beverified in an image acquisition area of the image acquisition deviceand/or the relative angle between the face to be verified and the imageacquisition device.

For example, before performing the face liveness detection based onlight reflection characteristic or other face liveness detectionprocesses, the image acquisition conditions of the face to be verifiedcan be detected, and then whether or not the preset requirement of theimage acquisition condition is satisfied can be determined. The faceliveness detection based on light reflection characteristic or otherface liveness detection processes can be executed when the presetrequirement of the image acquisition condition is satisfied. Thus, thequality of the images (including the illumination images, the actionimages, etc.) for face liveness detection can be guaranteed, and thecorrectly detection of the face in the images can be guaranteed, so theaccuracy of the method for face liveness detection can be improved.

For example, according to the embodiment of the present disclosure,before the step S208 and/or during executing the step S208 (namely inthe process and/or before the step of determining whether or not thepreset requirement of the image acquisition condition of the face to beverified is satisfied), the method for face liveness detection SE200 (orSE300) can further comprise: S206: outputting first prompt information,in which the first prompt information is used for notifying the face tobe verified to be directly opposite to the image acquisition device andto be closer to the image acquisition device.

For example, the first prompt information can be outputted in anyappropriate form. Illustratively, the step S206 can include: outputtingthe first prompt information in the form of one or more selected fromvoice, image and text. For example, text prompt information such as“Please Face the Screen” (for example, facing the screen is equivalentto that of facing the image acquisition device) can be outputted on adisplay screen of a mobile terminal, or the voice prompt information“Please Face the Screen” can be given out by a loudspeaker of the mobileterminal.

Illustratively, the method for face liveness detection can beimplemented by an APP (i.e., software application) installed on themobile terminal, which is used as the device for face livenessdetection. For example, immediately after the APP is enabled, the firstprompt information can be outputted to prompt the user to maintain anappropriate relative positional relationship with the mobile terminal,so that a camera of the mobile terminal can acquire face images (forexample, ideal face images) suitable for face liveness detection. In oneexample, the first prompt information can be continuously orintermittently outputted before the preset requirement of the imageacquisition condition is satisfied.

For example, outputting of the first prompt information can instruct theuser to adjust the relative positional relationship between the devicefor face liveness detection and the user (for example, the face to beverified of the user) in time, and meanwhile, the interaction betweenthe user and the device for face liveness detection can also improve theuser experience.

For example, according to the embodiment of the present disclosure, thestep S208 can include: acquiring a real-time image of the face to beverified; displaying a preset region (e.g., a reference region) forcalibrating the image acquisition conditions and a face region in thereal-time image (e.g., a reference part of the face to be verified), inreal time; and determining whether or not the preset requirement of theimage acquisition condition is satisfied according to the face regiondetected in the real-time image, in which it is determined that thepreset requirement of the image acquisition condition is satisfied ifthe face region is in the preset region and a ratio between a size ofthe face region and a size of the real-time image is greater than afirst preset ratio threshold (for example, a ratio threshold), and it isdetermined that the preset requirement of an image acquisition conditionof the face to be verified is not satisfied if the face region is not inthe preset region or the ratio between the size of the face region andthe size of the real-time image is not greater than the first presetratio threshold. It should be understood that the size of the faceregion and the size of the real-time image respectively can be an areaof the face region and an area of the real-time image.

In one example, whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied can bedetermined according to the acquired images of the face to be verified.For example, the mobile terminal can acquire the real-time image with acamera of the mobile terminal and then can execute the face detection.The face region can be acquired by the face detection. The face regionof the face to be verified can be an image block including the face tobe verified. Whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied can bedetermined according to the position of the face region of the face tobe verified in the real-time image and the ratio between the size of theface region of the face to be verified and the size of the real-timeimage. For example, the preset region can be defined in the real-timeimage. For example, the position of the face to be verified in the imageacquisition area of the image acquisition device can be limited throughthe preset region. For example, the size of the face region of the faceto be verified can reflect the distance and the relative angle betweenthe face to be verified and the image acquisition device. For example,both the preset region and the first preset ratio threshold (forexample, a ratio threshold) can be set as required. No limitation willbe given here in the present disclosure.

For example, if the face region of the face to be verified is disposedin the preset region but the ratio between the size of the face regionof the face to be verified and the size of the real-time image is lessthan the first preset ratio threshold (e.g., two-thirds), the face to beverified can be too oblique relative to the image acquisition deviceand/or too far away from the image acquisition device; in such a case,it can be determined that the preset requirement of an image acquisitioncondition of the face to be verified is not satisfied.

Illustratively, the method for face liveness detection SE200 (or SE300)can further comprise: outputting first acquisition prompt information toprompt the face to be verified to be closer to the image acquisitiondevice if the ratio of the size of the face region of the face to beverified to the size of the real-time image is not greater than thefirst preset ratio threshold.

Optionally, the first acquisition prompt information can be outputted inthe form of one or more selected from voice, image and text. Forexample, if the ratio of the size of the face region of the face to beverified to the size of the real-time image is found to be not greaterthan the first preset ratio threshold, first acquisition promptinformation such as “Please be Closer to the Camera” (or “Please beCloser to the Mobile Phone”) can be displayed on the display screen.

According to the embodiment of the present disclosure, the step S208 caninclude: acquiring real-time images of the face to be verified;outputting a preset region for calibrating the image acquisitionconditions and a face region in the real-time image, in real time; anddetermining whether or not the preset requirement of the imageacquisition condition is satisfied according to the face region detectedin the real-time image, in which it is determined that the presetrequirement of the image acquisition condition is satisfied if the faceregion is disposed in the preset region and a ratio between a size ofthe face region and a size of the preset region is greater than a secondpreset ratio threshold, and in which it is determined that the presetrequirement of an image acquisition condition of the face to be verifiedis not satisfied if the face region is not disposed in the preset regionor the ratio between the size of the face region and the size of thepreset region is greater than a second preset ratio threshold is notgreater than the second preset ratio threshold. It should be understoodthat the size of the preset region can be an area of the preset region.

In one example, whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied can bedetermined according to the acquired images of the face to be verified.For example, the mobile terminal can acquire the real-time images with acamera of the mobile terminal and then can execute face detection. Theface region of the face to be verified can be acquired by the facedetection. The face region of the face to be verified can be an imageblock including the face to be verified. Whether or not the presetrequirement of the image acquisition condition of the face to beverified is satisfied can be determined according to the position of theface region of the face to be verified in the real-time image and theratio between the size of the face region and the size of the presetregion.

For example, the preset region can be displayed on the display screen.The relative position between the face to be verified and the screen canbe limited through the preset region. The size of the face region canreflect the distance and the relative angle between the face to beverified and the image acquisition device. For example, when a distancebetween the face to be verified and the screen is decreased, the size ofthe face region displayed on the screen can be increased, and thereforethe size of the face region displayed on the screen can be increased toa certain value that allowing the preset conditions to be satisfied whenthe face to be verified is close enough to the screen. Of course, thesize of the face region displayed on the screen in real time can also beadjusted to satisfy the preset conditions only when the face is closeenough to the screen. No limitation will be given herein. Both thepreset region and the second preset ratio threshold can be set asrequired, and no limitation will be given here in the presentdisclosure.

For example, if the face region of the face to be verified is disposedin the preset region but the ratio between the size of the face regionand the size of the preset region is less than the second preset ratiothreshold (e.g., two-thirds), the face to be verified can be too obliquerelative to the image acquisition device and/or too far away from theimage acquisition device. In such a case, it can be determined that thepreset requirement of an image acquisition condition of the face to beverified is not satisfied.

Illustratively, the method for face liveness detection SE200 (or SE300)can further comprise: outputting second acquisition prompt informationto prompt the face to be verified to be closer to the image acquisitiondevice if the ratio between the size of the face region and the size ofthe preset region is not greater than the second preset ratio threshold.

Optionally, the second acquisition prompt information can be outputtedin the form of one or more selected from voice, image and text. Forexample, if the ratio between the size of the face region and the sizeof the preset region is found to be not greater than the second presetratio threshold, prompt information such as “Please be Closer to theCamera” (or “Please be Closer to the Mobile Phone”) can be displayed onthe display screen.

For example, according to the embodiment of the present disclosure, themethod SE200 (or SE300) can further comprise: determining the relativepositional relationship between the face region and the preset region inreal time; and outputting third acquisition prompt information accordingto the relative positional relationship between the face region and thepreset region, so as to notify the change of the relative positionalrelationship between the face to be verified and the image acquisitiondevice, so as to allow the face to be verified to be closer to thepreset region.

For example, when the method and device for face liveness detectionprovided by the embodiment of the present disclosure are implemented bythe mobile terminal, the face region (for example, an image blockincluding the face extracted from the real-time image) and an icon(namely the preset region displayed on the screen in real time) forindicating the preset region can be displayed on the display screen ofthe mobile terminal in real time. The real-time display of the faceregion and the icon for indicating the preset region can provideconvenience for the user to know current image acquisition condition andthe difference between the current image acquisition condition and thepreset requirements, so that the user can adjust its pose or the imageacquisition device (or the device for face liveness detection comprisingthe image acquisition device) so as to enter the subsequent faceliveness detection processes as soon as possible. Therefore, displayingthe face region and the icon for indicating the preset region inreal-time can improve the user experience and improve the efficiency ofthe face liveness detection.

In addition, third acquisition prompt information can also be outputtedto prompt the user to change the relative positional relationshipbetween the face to be verified and the image acquisition device, sothat the face region can be closer to the preset region. Optionally, thethird acquisition prompt information can be outputted in the form of oneor more selected from voice, image and text. For example, if the faceregion is found to be not disposed in the preset region, promptinformation such as “Please be Closer to the Center of the Circle” (forexample, a circle icon on the display screen can be used as the presetregion) can be displayed on the display screen. In addition, an arrowpointing from the face region to the preset region can also be displayedon the display screen, so that the user can know how to move the user orthe image acquisition device to allow the face region to fall within thepreset region as soon as possible. For example, the adjustment promptinformation such as “Please be Closer to the Center of the Circle” andthe image prompt information such as the arrow can be simultaneouslydisplayed; for another example, one of the adjustment prompt informationsuch as “Please be Closer to the Center of the Circle” and the imageprompt information such as the arrow can be displayed.

For example, according to the embodiment of the present disclosure, thestep S208 (namely determining whether or not the preset requirement ofthe image acquisition condition of the face to be verified is satisfied)can further include: acquiring posture information (i.e., attitudeinformation) of the image acquisition device; and determining whether ornot the image acquisition device is vertically placed according to theposture information, and determining that the preset requirement of theimage acquisition condition of the face to be verified is not satisfiedif the image acquisition device is not vertically placed. For example,in a case that the image acquisition device is vertically placed andother preset requirement of the image acquisition condition of the faceto be verified is also satisfied, it can be determined that the presetrequirement of the image acquisition condition is satisfied. It shouldbe understood that the term that vertically placed can indicate that theattitude of the image acquisition device is in a range of an optimumstate or the attitude of the image acquisition device is the mostfrequently adopted attitude by the user.

For example, when the method for face liveness detection provided by theembodiment of the present disclosure is implemented by the mobile phone,the posture information of the image acquisition device (namely thecamera of the mobile terminal) can be measured by a gyroscope sensorand/or an acceleration sensor mounted in the mobile terminal. When themobile terminal is vertically placed, the image acquisition device isalso vertically placed. In such a case, ideal face images (i.e., faceimages suitable for face liveness detection) can be acquired. Thus, theattitude of the image acquisition device can be adopted as one of theimage acquisition conditions, and used for determining whether or notthe preset requirement of the image acquisition condition of the face tobe verified is satisfied.

For example, according to the embodiment of the present disclosure, themethod for face liveness detection SE200 (or SE300) can furthercomprise: increasing number of times for performing the illuminationliveness detection by one in the process of executing the stepsS210-S220 (or the steps S310-S320) each time (namely increasing numberof times for performing the illumination liveness detection by one), soas to obtain the number of times for performing the illuminationliveness detection; and after the step S220 (or S320) (namely after theillumination liveness detection result is obtained) and in a case thatthe illumination liveness detection result indicates that the face to beverified fails to pass the illumination liveness detection, the methodfor face liveness detection SE200 (or SE300) can further comprise:outputting second error information used for notifying a failure of theillumination liveness detection, determining whether or not the numberof times for performing the illumination liveness detection is greaterthan a second counting threshold, and returning to the step S230 or S330(namely determining whether or not the face to be verified passes theface liveness detection at least according to the illumination livenessdetection result) if the number of times for performing the illuminationliveness detection is greater than the second counting threshold, andreturning to the step S208 (namely determining whether or not the presetrequirement of the image acquisition condition of the face to beverified is satisfied again) or returning to the step S210 or S310(namely executing the illumination liveness detection again) if thenumber of times for performing the illumination liveness detection isnot greater than the second counting threshold. For example, if thenumber of times for performing the illumination liveness detection isnot greater than the second counting threshold and the action livenessdetection is executed before the illumination liveness detection, theaction liveness detection can also be executed again. The second errorinformation is used for notifying the failure of the illuminationliveness detection of the face to be verified.

For example, as similar to the action liveness detection, for theillumination face liveness detection (for example, the steps S210-S220as illustrated in FIG. 18 or the steps S310-S320 as illustrated in FIG.19), if the face to be verified fails to pass the illumination livenessdetection, the illumination liveness detection can also be executedagain. The principle and the advantages of re-executed the illuminationliveness detection are similar to those of re-executing the actionliveness detection. No further description will be given herein.

Illustratively, when the method for face liveness detection comprisesthe above-mentioned step S208, the process can be executed againbeginning from the step S208 (that is, the step of determining whetheror not the preset requirement of the image acquisition condition of theface to be verified is satisfied and the subsequent steps can beexecuted again).

For example, according to the embodiment of the present disclosure,before the step S210 (or S310) and/or in the process of executing thestep S210 (or S310), the method for face liveness detection SE200 (orSE300) can further comprise: outputting second prompt information (thatis, outputting second prompt information during and/or before acquiringthe illumination images), in which the second prompt information usedfor notifying the face to be verified to keep still within a preset timeperiod of the illumination liveness detection. For example, in theprocess of executing the step S220 (or S320), the second promptinformation can also be outputted. For example, the second promptinformation can be outputted during the entire time period of performingthe illumination liveness detection.

For example, the preset time period of the illumination livenessdetection can be the execution time of the illumination livenessdetection (for example, the steps S210-S220 as illustrated in FIG. 18 orthe steps S310-S320 as illustrated in FIG. 19). For another example, thepreset time period of the illumination liveness detection can also bethe execution time of the step S210 or S310 (i.e., time needed foracquiring the illumination images of the face to be verified irradiatedby the detection light). For example, in the process of executing theillumination liveness detection (for example, in the process of adoptingthe detection light to irradiate the face to be verified and acquiringthe illumination images of the face to be verified), the face to beverified can be prompted to keep still within this time period, so as toavoid the adversely impact on the illumination images and the faceliveness detection result.

Illustratively, in a case that the face to be verified moves duringacquiring the illumination images and a moving distance is beyond anallowable range, the above-mentioned movement can result in that thepreset requirement of the image acquisition condition of the face to beverified is not satisfied anymore, in such a case, the method for faceliveness detection can return to the step S206 or the step S208, namelyone or more of the following steps can be executed again: determiningwhether or not the preset requirement of the image acquisition conditionof the face to be verified is satisfied, outputting the first promptinformation, outputting various kinds of acquisition prompt information,etc.

Illustratively, the second prompt information can comprise count-downinformation corresponding to the preset time period of the illuminationliveness detection. Optionally, the count-down information can beimplemented in the form of one or more selected from text, dynamicalimage and voice. The count-down information can keep the user informedof the progress of the illumination liveness detection and can improvethe interactive experience of the user.

For example, description will be given below to a concreteimplementation of the method for face liveness detection provided by theembodiment of the present disclosure with reference to FIG. 20. Theapplication scene as illustrated in FIG. 20 is a scene for mobileterminal, but the method for face liveness detection provided by thepresent disclosure is not limited to be applied to the mobile terminal.

For example, as illustrated in FIG. 20, firstly, the text promptinformation such as “Please Face the Screen” can be displayed on thedisplay screen of the mobile terminal to prompt the user to allow theface to be verified directly opposite to the screen, and the icon forindicating the preset region (represented by a circle) and the faceregion detected on the basis of the real-time image are simultaneouslydisplayed on the display screen. The text such as “Please Face theScreen” and the icon for indicating the preset region can becontinuously displayed for at least one of the following cases, that is,the user changes the position and/or pose of the face of the user, andthe user changes the position and/or attitude of the mobile terminal.For example, the text such as “Please Face the Screen” and the icon forindicating the preset region can be kept unchanged. For example, boththe size and the position of the face region of the face to be verifiedcan be changed. For example, the continuously changed face region can bedisplayed in real time, which is in favor of viewing the face region forthe user. Secondly, when the preset requirement of the image acquisitioncondition is satisfied, the subsequent stages or steps (for example, theillumination liveness detection), can be performed.

In the process of illumination liveness detection, the text such as“Please Stay Still” can be displayed on the display screen (as shown bythe 2^(nd) and 3^(rd) images in FIG. 20) to prompt the user to staystill, and count-down information can also be simultaneously displayedon the display screen. For example, the count-down information can berepresented in the 3^(rd) image as illustrated in FIG. 20 by a circularprogress bar (for example, a colored progress bar) marked on the edge ofthe icon (namely a circle) for indicating the preset region.

After the illumination liveness detection is completed, the actionliveness detection can be performed. As shown by the 4^(th) image inFIG. 20, the text such as “Please Nod” is displayed on the displayscreen to prompt the user to execute the action corresponding to theaction instruction such as “Please Nod”.

Finally, the final face liveness detection result (for example, the textsuch as “Face Liveness Detection Passed”) is outputted on the displayscreen.

Description will be given below to the method for face livenessdetection provided by the embodiment of the present disclosure withreference to FIG. 21. FIG. 21 is an illustrative flow diagram of amethod for face liveness detection SE1200 provided by the embodiment ofthe present disclosure. As illustrated in FIG. 21, the method for faceliveness detection SE1200 comprises the following steps.

S1210: determining whether or not a preset requirement of an imageacquisition condition of the face to be verified is satisfied.

For example, the image acquisition condition at least comprises one ormore selected from a position of the face to be verified, a pose of theface to be verified and a size of the face to be verified in a real-timeimage acquired by an image acquisition device, but the embodiment of thepresent disclosure is not limited thereto.

For example, the face to be verified can be a true face of a livinghuman being; for example, the face to be verified can also be thecounterfeited face such as a face on a photo, a face displayed by ascreen or a human face mask.

Illustratively, the real-time image of the face to be verified can beacquired, and the position of the face to be verified in the real-timeimage can be determined. When it is determined that the position of theface to be verified to be at an appropriate position suitable for theface liveness detection (for example, at a position near a center of thereal-time image), the subsequently face liveness detection processes canbe performed (for example, step S1220) and the images acquired in thesubsequently face liveness detection processes can be used as faceimages described in this disclosure for obtaining the face livenessdetection result (for example, obtaining the illumination detectionresult).

For example, the position of the face to be verified in the imageacquired by the image acquisition device can refer to the coordinate ofa center point (e.g., a point indicating the tip of the nose) of theface to be verified in the entire face image, but the embodiment of thepresent disclosure is not limited thereto.

Illustratively, the image acquisition conditions of the face to beverified can further include a pose of the face to be verified and/orsize of the face to be verified in the image acquired by the imageacquisition device. For example, the pose of the face to be verified canbe obtained (for example, estimated) according to various conventionalor future face pose estimation algorithms. For example, the pose of theface can be represented by three kinds of angles of the face in athree-dimensional space, and the three kinds of angles are pitch, yawand roll which respectively represent the angle of longitudinal flip(for example, an rotation angle around X-axis of a Cartesian coordinatesystem), horizontal flip (for example, an rotation angle around Y-axisof a Cartesian coordinate system) and rotation in a plane (for example,an rotation angle around Z-axis of a Cartesian coordinate system). Forexample, in a case that the pitch, yaw and roll of the face to beverified are within pre-determined angle threshold, it can be determinedthat the preset requirement of the image acquisition condition of thepose of the face to be verified is satisfied.

It should be understood that the size of the face to be verified in thereal-time image acquired by the image acquisition device is relevant tothe distance between the face to be verified and the image acquisitiondevice. For example, when the face to be verified is farther from theimage acquisition device, the size of the face to be verified in thereal-time image is smaller.

S1220: acquiring face images of the face to be verified with the imageacquisition device when the preset requirement of the image acquisitioncondition is satisfied.

S1230: determining whether or not the face to be verified passes theface liveness detection according to the face images.

According to methods adopted by different face liveness detections, thedifferent types of face images can be acquired. For distinction, theimage of the face to be verified acquired during the illuminationliveness detection can be referred to as the illumination image, and theimage of the face to be verified acquired during the action livenessdetection can be referred to as the action image. For example, in thefollowing embodiment of the illumination liveness detection, theillumination images of the face to be verified under the irradiation ofdetection light (or illumination light) can be acquired; and in theembodiment of the action liveness detection, the action images of theface to be verified can be acquired. The methods for acquiring the faceimages and the methods for face liveness detections on the basis of theface images can refer to the following description.

For example, before the step of acquiring the face images fordetermining whether or not the face to be verified is a living body, theimage acquisition conditions (for example, the position of the face tobe verified, the pose of the face to be verified and the size of theface to be verified in the image) can be adjusted to satisfy the presetrequirements, for example, allowing the position of the face to beverified to be closer to the center of the image as much as possible,allowing the pose of the face to be verified to be upright as much aspossible, and allowing the size of the face to be verified to be not toolarge or too small (namely allowing the face to be verified to be nottoo close or too far away from the image acquisition device). In such acase, face images with good quality can be acquired, so the accuracy ofthe face liveness detection result can be guaranteed.

Illustratively, the method for face liveness detection provided by theembodiment of the present disclosure can be implemented by a device, anapparatus or a system comprising a memory and a processor.

For example, the method for face liveness detection provided by theembodiment of the present disclosure can be adopted by an imageacquisition end, for example, can be adopted by an image acquisition endof a financial system such as a bank management system or can be adoptedby a mobile terminal such as a smart mobile phone and a tablet PC.Alternatively, the method for face liveness detection provided by theembodiment of the present disclosure can also be adopted by a server(e.g., a cloud server) and a client. For example, images of the face tobe verified are acquired by the client; the acquired images aretransmitted to the server (e.g., a cloud server) by the client; the faceliveness detection result is obtained by the server (e.g., a cloudserver); and the face liveness detection result is received by theclient from the server. Because the server can have more powerful dataprocessing capability than the client, when the face liveness detectionresult is obtained by the server, the face liveness detection speed canbe improved and the user experience can be improved. In addition,because the server can have higher processing speed, a more complexalgorithm for face liveness detection can be adopted, and therefore,obtaining of the face liveness detection result by the server canimprove the accuracy of face liveness detection.

FIG. 22 is an illustrative flow diagram of a method for face livenessdetection SE1300 provided by further another embodiment of the presentdisclosure. The steps S1310 and S1320 of the method for face livenessdetection SE1300 as illustrated in FIG. 22 are respectivelycorrespondingly to the steps S1210 and S1220 of the method for faceliveness detection SE1200 as illustrated in FIG. 21. The steps S1310 andS1320 as illustrated in FIG. 22 can be understood by those skilled inthe art with reference to relevant description in FIG. 21. No furtherdescription will be given herein. According to the embodiment, the stepS1230 of the method for face liveness detection SE1200 can furtherinclude the steps S1332 and S1334 as illustrated in FIG. 22. Detaileddescription will be given below.

Illustratively, face images acquired during the step S1320 can includeone or more illumination images, which is acquired by an imageacquisition device, of the face to be verified under the irradiation ofdetection light (namely illumination light). For example, the step S1230of the method for face liveness detection SE1200 can corresponding tothe steps S1332 and S1334 of the method for face liveness detectionSE1300. The step S1332 comprising: obtaining an illumination livenessdetection result according to a light reflection characteristic of theface to be verified in one or more illumination images, so as todetermine whether or not the face to be verified belongs to a livingbody. The step S1334 comprising: determining whether or not the face tobe verified passes the face liveness detection at least according to theillumination liveness detection result. For example, in the step S1332,a plurality of illumination images can be analyzed to obtain the lightreflection characteristic of the face to be verified in the plurality ofillumination images, and hence the illumination liveness detectionresult can be obtained.

Illustratively, a light source can be adopted to emit the detectionlight toward the face to be verified, and the light source can becontrolled by a processor to emit the detection light. For example, thespecific implementation of the light source can refer to the foregoingembodiment of the device for face liveness detection, and the embodimentof the method for face liveness detection as illustrated in FIG. 18. Nofurther description will be given herein.

For example, the specific implementation of the steps S1332 and S1334can respectively refer to the steps S220 and S230 of the method for faceliveness detection as illustrated in FIG. 18. No further descriptionwill be given herein.

As described above, as the light reflection characteristic of the faceof living human (e.g., the three dimensional human face) is differentfrom the light reflection characteristic of an object such as a displayscreen or a photo, a true face of living human and a face displayed bythe screen or a face on the photo can be effectively distinguishedaccording to the light reflection characteristic. Therefore, the methodand device for face liveness detection for determining whether or notthe face to be verified belongs to a living body according to the lightreflection characteristic of the face to be verified under the detectionlight can effectively counteract screen attackers or photo attackers,and hence can improve the safety and the user experience of anauthentication system or similar systems employing the method and devicefor face liveness detection.

For example, according to the embodiment of the present disclosure, thestep S1320 can include: if the image acquisition conditions of the faceto be verified in the current image (for example, the image used fordetermining whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied) satisfythe preset requirements, outputting a detection light controlinstruction for controlling the light source to emit the detection lighttoward the face to be verified, and acquiring one or more illuminationimages.

For example, when the preset requirement of the image acquisitioncondition is satisfied, a processor (e.g., a processor of a mobileterminal) can control the light source (e.g., a display screen of themobile terminal) to emit the detection light, and the processor can alsocontrol the light source to change mode of the detection light. The modeof the detection light has been described above, so no furtherdescription will be given herein.

According to the embodiment of the present disclosure, the step S1320can further include: returning to the step S1310 (namely determiningwhether or not the preset requirement of the image acquisition conditionof the face to be verified is satisfied) if the face to be verifiedmoves during acquiring one or more illumination images (for example,during acquiring one or more illumination images with an imageacquisition device) and a moving distance is beyond an allowable range(for example, a preset distance threshold).

For example, in the process of acquiring the illumination images, theposition of the face to be verified, the pose of the face to be verifiedand the size of the face to be verified in a real-time image can becontinuously monitored. For example, if the face to be verified moves tothe left and the moving distance is beyond the preset distancethreshold, the step S1310 is returned to determine the image acquisitionconditions again so as to acquire new illumination images. For example,the step S1310 and the step of acquiring the illumination images can berepeated until the sufficient number of illumination images, which isacquired under a condition that the preset requirement of the imageacquisition condition is satisfied, are acquired; in such a case, thequality of the illumination images for face liveness detection can beensured, and the image acquisition conditions such as the position ofthe face to be verified, the pose of the face to be verified and thesize of the face to be verified in a real-time image can be sufficientlystable, so that the accuracy of the face liveness detection result canbe sufficiently high.

For example, the attack methods adopted by the attackers can be various,although the face liveness detection on the basis of light reflectioncharacteristic can counteract screen attack or photo attack, the methodfor the face liveness detection on the basis of light reflectioncharacteristic can fail to counteract some other attack methodsadopting, for example, a three dimensional human face mask. Therefore,in order to further refine the method for face liveness detection andimprove the safety of the method for the face liveness detection, otherface liveness detection processes (e.g., action liveness detection) canbe further incorporated into the method for face liveness detection onthe basis of the light reflection characteristic. One illustrativeembodiment will be described below.

FIG. 23 is an illustrative flow diagram of a method for face livenessdetection SE1400 provided by further another embodiment of the presentdisclosure. The steps S1410 and S1430 of the method for face livenessdetection SE1400 as illustrated in FIG. 23 correspond to the steps S1310and S1332 of the method for face liveness detection SE1300 asillustrated in FIG. 22. The steps S1410 and S1430 as illustrated in FIG.23 can be understood by those skilled in the art with reference torelevant description in FIG. 22. No further description will be givenherein. According to the embodiment, when image acquisition conditionsof the face to be verified satisfy preset requirements, obtaining faceimages of the face to be verified acquired by an image acquisitiondevice, in which obtaining of face images of the face to be verified caninclude the steps S1420 and S1450 as illustrated in FIG. 23, namelyacquiring a plurality of illumination images of the face to be verifiedand acquiring an action image of the face to be verified when the presetrequirement of the image acquisition condition is satisfied. Forexample, the step S1334 of the method for face liveness detection SE1300as illustrated in FIG. 22 corresponds to the step S1480 in theembodiment. In addition, before performing the step S1480, the methodfor face liveness detection SE1400 can further comprise the steps S1440,S1450, S1460 and S1470.

For example, the illumination images and the action images of the faceto be verified can be respectively acquired in the steps S1420 andS1450. The acquisition method of the illumination images of the face tobe verified can refer to the above-mentioned description, and no furtherdescription will be given here. The following mainly describes theaction liveness detection (namely the steps S1440-S1470).

For example, an action instruction can be outputted in the step S1440,in which the action instruction is used for used for notifying the faceto be verified to execute an action corresponding to the actioninstruction.

For example, the action image of the face to be verified acquired by theimage acquisition device can be acquired in the step S1450. For example,the face images can further include the action image.

S1460: detecting the action executed by the face to be verifiedaccording to the action images, so as to obtain an action detectionresult.

S1470: obtaining an action liveness detection result according to theaction detection result and the action instruction.

S1480: determining whether or not the face to be verified passes theface liveness detection according to both of the illumination livenessdetection result and the action liveness detection result.

Illustratively, if the face to be verified passes both the illuminationliveness detection and the action liveness detection (namely both theillumination liveness detection result and the action liveness detectionresult indicate that the face to be verified belongs to a living body),it is determined that the face to be verified passes the face livenessdetection; and if the face to be verified fails to pass the illuminationliveness detection or the action liveness detection (namely if any ofthe illumination liveness detection result and the action livenessdetection result indicates that the face to be verified does not belongto a living body), it is determined that the face to be verified failsto pass face liveness detection. It should be understood that theabove-mentioned methods are only illustrative, and there are otherdetermination methods for determining whether or not the face to beverified passes face liveness detection.

It should be noted that that the execution sequence of the actionliveness detection (for example, the steps S1440-S1470) and theillumination liveness detection (for example, the steps S1420-S1430) canbe set according to specific implementation demands. No specificlimitation will be given here in the present disclosure.

For example, the executing sequence of the method for face livenessdetection SE1400 can be S1410, S1420, S1430, S1440, S1450, S1460, S1470and S1480 (in such a case, the illumination liveness detection can beperformed before performing the action liveness detection), but theembodiment is not limited to the case; for another example, theexecuting sequence of the method for face liveness detection SE1400 canalso be S1440, S1450, S1460, S1470, S1410, S1420, S1430 and S1480 (insuch a case, the illumination liveness detection can be performed afterthe action liveness detection is performed); for further anotherexample, the executing sequence of the method for face livenessdetection SE1400 can also be S1410, S1440, S1450, S1460, S1470, S1410,S1420, S1430 and S1480; for still another example, the executingsequence of the method for face liveness detection SE1400 can also beS1410, S1440, S1450, S1460, S1470, S1420, S1430 and S1480.

It should be understood that he method for face liveness detectionSE1400 can comprise steps (for example, outputting second promptinformation) other than the steps S1410-S1480, the concrete content canrefer to the embodiments of the present disclosure, no furtherdescription will be given herein.

For example, the method for face liveness detection based on actionliveness detection can be independently implemented by an imageacquisition end, for example, can be independently implemented by animage acquisition end of a financial system such as a bank managementsystem or a mobile terminal such as a smart mobile phone and a tabletPC. Alternatively, the method for face liveness detection based onaction liveness detection can also be implemented by a server (e.g., acloud server) and a client together. For example, the action instructioncan be generated by the server or the client; the action images of theface to be verified can be acquired by the client; the acquired actionimages can be transmitted to the server (e.g., a cloud server) by theclient; the action liveness detection result can be obtained by theserver (e.g., a cloud server); and then the action liveness detectionresult can be received by the client.

For example, the final face liveness detection result can be outputtedafter both the illumination liveness detection and the action livenessdetection are completed; in such a case, attackers are unable todetermine the liveness detection method preventing the attack fromsucceeding (for example, the method preventing the attack fromsucceeding can be the illumination liveness detection and/or the actionliveness detection), so the safety of the method for face livenessdetection can be further improved. The face liveness detection based onthe action liveness detection processes and the illumination livenessdetection can counteract various attack methods (such as mask attack),such that the safety of the method and the device (or system) for faceliveness detection and relevant authentication system incorporated withthe device (or system) for face liveness detection can be furtherimproved.

For example, according to the embodiment of the present disclosure, thestep S1470 (namely obtaining the action liveness detection resultaccording to the action detection result and the action instruction) caninclude: determining that the face to be verified passes the actionliveness detection in a case that an action, which is executed by theface to be verified and matched with the action instruction, is detectedin the action image, which is acquired within a time period not greaterthan a preset time period of the action liveness detection, anddetermining that the face to be verified fails to passes the actionliveness detection in a case that the action, which is executed by theface to be verified and matched with the action instruction, is notdetected in the action image, which is acquired within the time periodnot greater than the preset time period of the action livenessdetection.

According to the embodiment of the present disclosure, in the process ofexecuting the step S1440 (namely during outputting the actioninstruction), the method for face liveness detection SE1400 can furthercomprise: outputting first time prompt information, in which the firsttime prompt information includes count-down information corresponding tothe preset time period of the action liveness detection.

According to the embodiment of the present disclosure, before the stepS1332 (or S1420) and/or in the process of executing the steps S1332 (orS1420), namely in the process and/or before acquiring the illuminationimages, the method for face liveness detection SE1300 (or SE1400) canfurther comprise: outputting second prompt information, in which thesecond prompt information is used for notifying the face to be verifiedto keep still within the preset time period of the illumination livenessdetection. For example, in the process of executing the step S1334 (orS1430), the second prompt information can also be outputted. Forexample, the second prompt information can be outputted during theentire time period of performing the illumination liveness detection.

For example, the preset time period of the illumination livenessdetection can be the execution time of the illumination livenessdetection (the steps S1332-S1334 as illustrated in FIG. 21 or the stepsS1420-S1430 as illustrated in FIG. 23). For another example, the presettime period of the illumination liveness detection can also be theexecution time of the step S1332 (or S1420) (i.e., time needed foracquiring the illumination images of the face to be verified).

Illustratively, the step S1310 (or S1410) can be returned (namelywhether or not the preset requirement of the image acquisition conditionof the face to be verified is satisfied is determined again) if thepreset requirement of the image acquisition condition of the face to beverified is not satisfied, which is caused by the movement of the faceto be verified within the preset time period of the illuminationliveness detection.

The specific content can refer to relevant content in the method forface liveness detection SE300 as illustrated in FIG. 19. No furtherdescription will be given herein.

For example, according to the embodiment of the present disclosure,before the step S210 (S1310 or S1410), namely before the step ofdetermining whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied, or beforethe step of adopting the image acquisition device to acquire thereal-time image of the face to be verified, the method for face livenessdetection SE1200 (SE1300 or SE1400) can further comprise: outputtingfirst prompt information, in which the first prompt information is usedfor notifying the face to be verified to be directly opposite to theimage acquisition and to be closer to the image acquisition device.

The implementations of the step S1210 (S1310 or S1410), namelydetermining whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied, can bevarious. Several concrete embodiments of the step S1210 (S1310 or S1410)will be described below, but the embodiment of the present disclosure isnot limited thereto. In several following concrete embodiments, a simpleinteractive command is adopted to instruct the user to execute correctoperation, so the convenience of the method for face liveness detectioncan be improved.

For example, determining of whether or not the preset requirement of theimage acquisition condition of the face to be verified is satisfiedcomprises: adopting the image acquisition device to acquire thereal-time image of the face to be verified; displaying a reference partof the face to be verified in the real-time image and a reference regionin real time; and determining whether or not the preset requirement ofthe image acquisition condition is satisfied at least according towhether or not the reference part of the face to be verified in thereal-time image falls within the reference region. For example, thereference part of the face to be verified in the real-time image and thereference region are respectively the face to be verified in thereal-time image and the face preview region; for another example, thereference part of the face to be verified in the real-time image and thereference region are respectively a specific facial part of the face tobe verified in the real-time image and a targeted part region.

According to an embodiment of the present disclosure, the step S1210(S1310 or S1410) can include: acquiring the real-time image of the faceto be verified acquired by the image acquisition device; displaying aface preview region in real time, and displaying part of the real-timeimage in the face preview region in real time, in which, the displayedpart of the real-time image is consistent with the face preview regionin position, for example, the displayed part of the real-time image andthe face preview region are completely coincide with each other inposition; displaying adjustment prompt information in real timeaccording to the image acquisition conditions, which is obtained basedon the real-time image, of the face to be verified, in which theadjustment prompt information is used for notifying the face to beverified to make adjustment allowing the preset requirement of the imageacquisition condition to be satisfied; and determining whether or notthe preset requirement of the image acquisition condition of the face tobe verified is satisfied at least according to whether or not theposition of the face to be verified in the real-time image falls withina range defined by the face preview region.

FIG. 24 is a schematic diagram illustrating the content displayed on thedisplay screen during the implementation of the method for face livenessdetection provided by one embodiment.

For example, as illustrated in FIG. 24, at the beginning of the faceliveness detection process, a face preview region and a text prompt areaare displayed on the display screen. For example, both the position ofthe face preview region and the position of the text prompt area can befixed. It should be noted that the text “Text Prompt Area” displayed onthe leftmost image in FIG. 24 is only illustrative, and the textactually displayed in the text prompt area can be preset and can also bedetermined according to the current condition of the face to beverified. For example, when the face to be verified fails to fall withinthe face preview region, the text displayed in the text prompt area canbe text such as “Please Let Me See Your Face”. Similarly, the “Facepreview region” displayed on the leftmost image in FIG. 24 isillustrative, and the content actually displayed in the face previewregion can be part of the current acquired image, in which, thedisplayed part of the current acquired image is consistent with the facepreview region in position. It should be understood that the consistencyin position (i.e., the displayed part of the current acquired image isconsistent with the face preview region in position) described in thepresent disclosure refers to that the coordinate of the face previewregion displayed on the display screen is consistent with the coordinateof the displayed part of the current acquired image, acquired by theimage acquisition device, in the current acquired image. It should beunderstood it is not necessary to set the text prompt area in a case ofusing voice to realize prompt and interactive function.

For example, part of the real-time image can be displayed in the facepreview region in real time, in which, the displayed part of thereal-time image is consistent with the face preview region in position.It should be understood that the content displayed in the face previewregion can be a video in a case of continuously acquiring a plurality ofreal-time images. When the scene object in the image acquisition areachanges (for example, when the face to be verified moves), the displayedcontent in the face preview region can also be changed along with themovement of the face to be verified. For example, the user canintuitively determine whether or not the face to be verified is locatedin an appropriate position, whether or not the size of the face to beverified is appropriate, whether or not the pose of the face to beverified is appropriate, and the like through the displayed content inthe face preview region.

For example, during determining whether or not the preset requirement ofthe image acquisition condition of the face to be verified is satisfied(for example, in the process of executing the step S1210), a displayedcolor of the display screen except for the face preview region and thetext prompt area can be white, so as to highlight the face previewregion and the text prompt area.

For example, with reference to the middle image as illustrated in FIG.24, at this point, the stage of the face liveness detection based onlight reflection characteristic, namely the stage of the illuminationliveness detection, is entered; adjustment prompt information such as“Please Stay Still” is displayed in the text prompt area and meanwhile,count-down information can be displayed at the edge of the face previewregion. For example, the count-down information can be displayed by aprogress bar; for example, the progress bar can be a circular progressbar (for example, a circular progress bar overlapped with an edge of acircular face preview region); and for another example, the progress barcan also be a colored progress bar. For example, in this stage, the faceto be verified still can be displayed in the face preview region in realtime.

For example, with reference to the rightmost image as illustrated inFIG. 24, at this point, the stage of action liveness detection isentered, and the action instructions such as “Please Nod Aggressively”is displayed in the text prompt area to prompt the user to execute theaction corresponding to the action instruction. In this stage, the faceto be verified can also be continuously displayed in the face previewregion in real time.

For example, the implementation form and the displayed content of theface preview region and the text prompt area described above and shownin FIG. 24 can be set as required. No limitation will be given here inthe application.

For example, according to the above-mentioned embodiment, theimplementation difficulty and the amount of calculation can be reducedby adoption of a face preview region with a fixed position as areference for the user to align the face to be verified and by adoptionof the text prompt area to prompt the user to make adjustment allowingthe preset requirement of the image acquisition condition to besatisfied.

For example, according to the embodiment of the present disclosure, thestep of displaying the adjustment prompt information in real timeaccording to the image acquisition conditions of the face to be verifiedin the real-time image (for example, outputting of the adjustment promptinformation in the case that the preset requirement of the imageacquisition condition is not satisfied) can include one or more of thefollowing: outputting the adjustment prompt information in a case thatthere is no face to be verified in the real-time image, so as to notifythe person having the face to be verified to move toward a directionallowing the face to be verified to be in the real-time image; andoutputting the adjustment prompt information in a case that the positionof the face to be verified in the real-time image is deviated from aface preview region, so as to notify the person having the face to beverified to move towards a direction opposite to a deviation direction.

For example, adjustment prompt information such as “Please Let Me SeeYour Face” can be outputted when the face to be verified does not fallwithin the image acquisition area and the face to be verified cannot befound in the real-time image. For example, adjustment prompt informationsuch as “Please Move Right a Little” can be outputted when the positionof the face to be verified in the real-time image is on the leftrelative to the face preview region (for example, when the center of theface to be verified is on the left of the center of face previewregion). For example, adjustment prompt information such as “Please MoveLeft a Little” can be outputted when the position of the face to beverified in the real-time image is on the right relative to the facepreview region (for example, when the center of the face to be verifiedis on the right of the center of face preview region). For example,adjustment prompt information such as “Please Move Down a Little” can beoutputted when the position of the face to be verified in the real-timeimage is on the upper side relative to the face preview region (forexample, when the center of the face to be verified is on the upper sideof the center of face preview region). For example, adjustment promptinformation such as “Please Move Up a Little” can be outputted when theposition of the face to be verified in the real-time image is on thelower side of the face preview region (for example, when the center ofthe face to be verified is on the lower side of the center of facepreview region).

The above-mentioned adjustment prompt information is only illustrative,and the adjustment prompt information displayed in the text prompt areacan be set as required. No limitation will be given here in the presentdisclosure.

For example, according to the embodiment of the present disclosure, theimage acquisition conditions can also include a blurriness of thereal-time image and a shielding state of the face to be verified in thereal-time image; and the step of displaying the adjustment promptinformation in real time according to the image acquisition conditionsof the face to be verified in the real-time image (for example,outputting of the adjustment prompt information in the case that thepreset requirement of the image acquisition condition is not satisfied)can include one or more of the following: outputting the adjustmentprompt information in a case that a blurriness of the real-time imageexceeds a preset blurriness threshold, so as to notify user to clean theimage acquisition device; outputting the adjustment prompt informationin a case that the pose of the face to be verified in the real-timeimage is in a face upward state, so as to notify the person having theface to be verified to lower his/her head; outputting the adjustmentprompt information in a case that the pose of the face to be verified inthe real-time image is in a face downward state, so as to notify theperson having the face to be verified to raise his/her head; outputtingthe adjustment prompt information in a case that the pose of the face tobe verified in the real-time image is tilting to the left or the right,so as to notify the person having the face to be verified to lookstraight ahead; outputting the adjustment prompt information in a casethat the size of the face to be verified in the real-time image is lessthan a first threshold, so as to notify the person having the face to beverified to be closer to the image acquisition device; outputting theadjustment prompt information in a case that the size of the face to beverified in the real-time image is greater than a second threshold, soas to notify the person having the face to be verified to be away fromthe image acquisition device; and outputting the adjustment promptinformation in a case that a specific facial part of the face to beverified in the real-time image is shielded by an occlusion, so as tonotify the person having the face to be verified to remove the occlusionand to expose the specific facial part.

As described above, the image acquisition conditions of the face to beverified can also include the pose and/or size of the face to beverified. Correspondingly, when the image acquisition conditions of theface to be verified also include the pose and/or size of the face to beverified, the adjustment prompt information relevant to the adjustmentof the pose and/or size of the face to be verified can be outputted.

For example, adjustment prompt information such as “Please Lower YourHead Slightly” is outputted when the pose of the face to be verified inthe real-time image is in a face upward state. For example, adjustmentprompt information such as “Please Raise Your Head Slightly” isoutputted when the pose of the face to be verified in the real-timeimage is in a face downward state. Adjustment prompt information such as“Please Look Straight Ahead” is outputted when the pose of the face tobe verified in the real-time image is tilting to the left or the right.Adjustment prompt information such as “Please be Closer to the Screen”is outputted when the size of the face to be verified in the real-timeimage is too small. For example, adjustment prompt information such as“Please Stay away from the Screen” is outputted when the size of theface to be verified in the real-time image is too large.

For example, other factors that can affect the accuracy of the faceliveness detection can also be taken as one or more of the imageacquisition conditions described in the present disclosure. For example,the image acquisition conditions of the face to be verified can alsoinclude a blurriness of the real-time image and a shielding state of theface to be verified in the real-time image. For example, adjustmentprompt information such as “Do not Block Your Eyes” is outputted whenthe eyes of the face to be verified in the real-time image are blockedby other objects (e.g., an occlusion such as the hair, the hand orglasses). Adjustment prompt information such as “Do not Cover YourMouth” is outputted when the mouth of the face to be verified in thereal-time image is covered by other objects (e.g., an occlusion such asa gauze mask or the hand). For example, adjustment prompt informationsuch as “Please Wipe the Lens” can be outputted to prompt the user toclean the image acquisition device when the blurriness of the real-timeimage exceeds the preset blurriness threshold.

Illustratively, it can be preset as required that, the next stage (e.g.,the stage of the illumination liveness detection) can be entered and thepreset requirement of the image acquisition condition can be satisfiedonly when the face to be verified is entirely within the face previewregion, the ratio between the size of the face to be verified and thesize of the entire face preview region is within a preset ratio range(the preset ratio range can be, for example, more than 70%), and theinclination angle of the face to be verified in any direction of thethree-dimensional space does not exceed an angle preset threshold.

Illustratively, as illustrated in FIG. 24, the adjustment promptinformation can be displayed in an area above the face preview region;in such a case, the user's eyes can be allowed to focus on this area,and thus the user does not need to change a direction of the line ofsight, so the experience can be consistent and smooth, and theinteractive progress can be simple and clear, and hence good interactiveexperience can be obtained.

For example, determining of whether or not the preset requirement of theimage acquisition condition of the face to be verified is satisfiedcomprises: adopting the image acquisition device to acquire thereal-time image of the face to be verified; displaying a reference partof the face to be verified in the real-time image and a reference regionin real time; and determining whether or not the preset requirement ofthe image acquisition condition is satisfied at least according towhether or not the reference part of the face to be verified in thereal-time image falls within the reference region. For example, thereference part of the face to be verified in the real-time image and thereference region are respectively the face to be verified in thereal-time image and the face preview region; for another example, thereference part of the face to be verified in the real-time image and thereference region are respectively a specific facial part of the face tobe verified in the real-time image and a targeted part region.

Description will be given below with reference to FIG. 25 to a method ofdetermining whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied and amethod of instructing the user to align some specific facial parts ofthe face to be verified with the targeted part region in the case thatthe preset requirement of the image acquisition condition of the face tobe verified is not satisfied, provided by the embodiment of the presentdisclosure.

According to the embodiment of the present disclosure, the step S1210(S1310 or S1410), namely the step of determining whether or not thepreset requirement of the image acquisition condition of the face to beverified is satisfied can include: adopting the image acquisition deviceto acquire acquiring the real-time image of the face to be verified;displaying a targeted part region in real time, and displaying the faceto be verified in the real-time image in real time; and determiningwhether or not a specific facial part of the face to be verified of thereal-time image falls within the targeted part region, determining thatthe preset requirement of the image acquisition condition is satisfiedif the specific facial part of the face to be verified of the real-timeimage falls within the targeted part region, and determining that thepreset requirement of the image acquisition condition of the face to beverified is not satisfied if the specific facial part of the face to beverified of the real-time image fails to fall within the targeted partregion.

For example, when the preset requirement of the image acquisitioncondition of the face to be verified is not satisfied, the user can alsobe instructed to align some specific facial parts of the face to beverified with the targeted part region, such that the alignment of theface to be verified can be realized, and the preset requirement of theimage acquisition condition of the face to be verified can be satisfied.For example, in the alignment process, the ultimate goal of aligning theentire face can be achieved by requiring the user to align relativeobvious specific facial parts of the face to be verified with specifiedpositions (for example, the targeted part region) displayed on thedisplay screen.

FIG. 25 is a schematic diagram illustrating the content displayed on thedisplay screen during the implementation of the method for face livenessdetection in the embodiment of the present disclosure. For example, inthe example as illustrated in FIG. 25, the user can be required to alignboth eyes of the face to be verified to specified positions (forexample, the targeted part region), and once the operation of aligningthe both eyes is completed, it can be determined that the entirely faceto be verified is aligned. For example, the alignment method can also berealized by aligning other specific facial parts with obvious featuressuch as the tip of nose, the place between the eyebrows, and the jawwith the targeted part region. For example, the face to be verified canalso be aligned by simultaneous aligning of a plurality of specificfacial parts with specified positions (for example, the targeted partregions), for example, by simultaneous aligning both eyes and the tip ofnose with specified positions.

For example, the leftmost image of FIG. 25 shows a targeted part region.For example, the targeted part region can be two regions defined bycircles. For example, if both eyes of the face to be verified is withinthe targeted part region, namely the both eyes are within the displayedcircles, it can be determined that the preset requirement of the imageacquisition condition is satisfied; and the case illustrated in therightmost image of FIG. 25 shows that the both eyes of the face to beverified is within the targeted part region. For example, if not all ofthe both eyes of the face to be verified is within the targeted partregion (for example, if only one eye is within the targeted part regionor only part of each eye is within the targeted part region, namely botheyes are not aligned with the displayed circles), it can be determinedthat the preset requirement of the image acquisition condition of theface to be verified is not satisfied.

For example, as illustrated in FIG. 25, in the process of aligning thespecific facial part, other parts of the face to be verified, except forthe specific facial part (for example, the face to be verified exceptfor both eyes), can also be displayed in real time. Thus, the user caneasily determine the distance difference between the specific facialpart and the targeted part region, so the alignment of the face to beverified can be conveniently performed. For example, because it'sdifficult to require the user to directly adjust the parameters such asthe pose and the size of the face to be verified without a reference,providing a specific reference (for example, the targeted part region)can lower the difficulty of realizing the alignment of the face to beverified, so the alignment method in the embodiment is easier toexecute, and hence the use experience of the user can be improved.

Description will be given below with reference to FIG. 26 to anothermethod provided by the embodiment of the present disclosure fordetermining whether or not the preset requirement of the imageacquisition condition of the face to be verified is satisfied and amethod of instructing the user to adjust the face to be verified in thecase that it is determined that the preset requirement of the imageacquisition condition of the face to be verified is not satisfied, sothat the preset requirement of the image acquisition condition of theface to be verified can be satisfied.

According to the embodiment of the present disclosure, the step S1210(S1310 or S1410), namely the step of determining whether or not thepreset requirement of the image acquisition condition of the face to beverified is satisfied includes: adopting the image acquisition device toacquire the real-time image of the face to be verified; displaying asimulated face region changing along with the face to be verified inreal time according to the image acquisition condition, wherein the faceto be verified is displayed in the simulated face region; displaying atargeted face region, which is used for indicating an alignment of theface to be verified, in real time; and determining whether or not thesimulated face region is aligned with the targeted face region, in whichit is determined that the preset requirement of the image acquisitioncondition of the face to be verified is satisfied in a case that thesimulated face region is aligned with the targeted face region, and itis determined that the preset requirement of the image acquisitioncondition of the face to be verified is not satisfied in a case that thesimulated face region is not aligned with the targeted face region.

A real-time, abstract or concrete virtual object (e.g., the simulatedface region) can be generated for the alignment of the face to beverified according to the image acquisition conditions (e.g., theposition, the pose and the size of the face to be verified) of the faceto be verified in each real-time image. In the alignment process, themainly considered factor can comprise at least one of the followingfactors (for example, all of the following factors): whether or not thepose of the face to be verified is put in a upright state, whether ornot the center of the face to be verified is overlapped with the centerof, for example, the real-time image, whether or not the size of theface to be verified is appropriate, and the like. These imageacquisition conditions can be calculated by algorithms; the calculatedimage acquisition conditions are mapped as key values by functions; andthese key values are used to control the virtual object (e.g., thesimulated face region) displayed on the display screen to move orchange. For example, the position of the face to be verified in thereal-time image can be mapped as a position of the virtual object (e.g.,the simulated face region) in the display screen, such that the positionof the virtual object can be controlled by the position change of theface to be verified in the real-time image. For another example, thesize of the face to be verified in the real-time image can be mapped asa size of the virtual object (e.g., the simulated face region), suchthat the size of the virtual object can be controlled by the size of theface to be verified in the real-time image. For further another example,the pose of the face to be verified in the real-time image can be mappedas a shape of the virtual object (e.g., the simulated face region) inthe display screen, such that the shape of the virtual object can becontrolled by the pose change of the face to be verified in thereal-time image. For example, the virtual object can be a circularobject in a case that the face to be verified is a front view face, andthe virtual object can be an elliptic object in a case that the face tobe verified is a side view face. It should be understood that the shapeof the virtual object (e.g., the simulated face region) is not limitedto circle and ellipse, the shape of the virtual object can be setaccording to specific implementations; no limitation will be givenherein. For example, the shape of the virtual object (e.g., thesimulated face region) can also be a square or in a shape of heart. Itshould be understood that the mapping between the image acquisitionconditions and the key values corresponding to the virtual object (e.g.,the simulated face region) can be set according to specificimplementations; no limitation will be given herein. For example, thesize of the virtual object can be the same as or half of the size of theface to be verified in the real-time image, but the present disclosureis not limited to the case. For example, the size of the face to beverified can be a minimum value of a diameter of a circle which canencircle the face to be verified.

For example, it is determined that the preset requirement of the imageacquisition condition of the face to be verified is satisfied in a casedthat the virtual object controlled by the image acquisition conditionsreaches a preset target state. For example, reaching of the presettarget state can be any one of the following cases: the virtual objectis within the targeted face region; the virtual object is within thetargeted face region and the size of the virtual object is within apreset size range (for example, the size of the virtual object allows aratio between the size of the virtual object and the size of thetargeted face region to be greater than ⅔); the virtual object is withinthe targeted face region, the size of the virtual object is within apreset size range and the ellipticity of the virtual object is within apreset ellipticity range (for example, the ellipticity of the virtualobject is less than 10%), in which the ellipticity of the virtual objectcan be calculated by the following equation, that is,ellipticity=2×100%×(major axis of the ellipse−minor axis of theellipse)/(major axis of the ellipse+minor axis of the ellipse).

For example, FIG. 26 is a schematic diagram illustrating the contentdisplayed on the display screen during the implementation of the methodfor face liveness detection in the embodiment of the present disclosure.For example, in FIG. 26, an image window for indicating the face to beverified (namely the simulated face region; the image window is a videowindow when a plurality of continuous images are acquired for the faceto be verified) is designed to be a circular object capable of movingand capable of changing the size. The user can control the circularobject (or control the simulated face) to move up, down, left or rightby adjusting the angle between the face to be verified and the displayscreen (and the image acquisition device), and the position of the faceto be verified in the display screen. The user can also adjust the sizeof the circular object by adjusting the distance between the face to beverified and the display screen (and the image acquisition device). Forexample, the ultimately goal is to require the user to align thecircular object with the targeted face region (for example, a circularring with largest diameter as illustrated in FIG. 26), namely allowingthe circular object to be within the circular ring with largestdiameter.

It should be understood that the continuous images in the presentapplication indicates that the number of the images displayed on thedisplay screen per minute is sufficiently high (for example, 24 imagesper minute) such that the user takes the displayed images as thecontinuous images.

In one example, it can be determined that the simulated face region isaligned with the targeted face region when the edge of the simulatedface region and the edge of the targeted face region are completelyaligned (for example, completely overlapped with each other). In anotherexample, it can be determined that the simulated face region is alignedwith the targeted face region when the simulated face region fallswithin the targeted face region and the ratio of the size of thesimulated face region to the size of the targeted face region (namelythe proportion of the simulated face region in the targeted face region)is within the range of the preset ratio (for example, not greater thanthe second preset ratio described in the present disclosure).

For example, according to the embodiment of the present disclosure, thestep of determining of whether or not the simulated face region isaligned with the targeted face region can comprise: determining that thesimulated face region is aligned with the targeted face region in a casethat the simulated face region is within the targeted face region and aratio of a size of the simulated face region to a size of the real-timeimage is greater than a first preset ratio; and determining that thesimulated face region is not aligned with the targeted face region in acase that the simulated face region is not within the targeted faceregion or the ratio of the size of the simulated face region to the sizeof the real-time image is less than or equal to the first preset ratio.

The size of the simulated face region can reflect the distance and therelative angle between the face to be verified and the image acquisitiondevice, so the size of the simulated face region can be used as adetermination condition regarding whether or not the image acquisitionconditions satisfy the requirements. Both the targeted face region andthe first preset ratio can be set as required. No limitation will begiven here in the present disclosure.

For example, if the simulated face region of the face to be verified iswithin the targeted face region but the ratio of the size of thesimulated face region to the size of the real-time image is less thanthe first preset ratio (e.g., two-thirds), the pose of the face to beverified can be too oblique and/or the face to be verified can be toofar away from the image acquisition device. In such a case, it can bedetermined that the image acquisition conditions do not satisfy therequirements.

Illustratively, the method for face liveness detection SE1200 (SE1300 orSE1400) can further comprise: outputting first acquisition promptinformation in real time to prompt the face to be verified to be closerto the image acquisition device when the ratio of the size of thesimulated face region to the size of the real-time image is not greaterthan the first preset ratio.

According to the embodiment of the present disclosure, the step ofdetermining of whether or not the simulated face region is aligned withthe targeted face region can comprise: determining that the simulatedface region is aligned with the targeted face region in a case that thesimulated face region is within the targeted face region and a ratio ofa size of the simulated face region to a size of the targeted faceregion is greater than a second preset ratio; and determining that thesimulated face region is not aligned with the targeted face region in acase that the simulated face region is not within the targeted faceregion or the ratio of the size of the simulated face region to the sizeof the targeted face region is less than or equal to the second presetratio.

For example, the size of the simulated face region can reflect thedistance and the relative angle between the face to be verified and theimage acquisition device, so the size of the simulated face region canbe used as a determination condition regarding whether or not the imageacquisition conditions satisfy the requirements For example, when thedistance between the face to be verified is decreased, the size of thesimulated face region displayed on the display screen in real time isincreased, and therefore, the size of the simulated face regiondisplayed on the display screen can be increased to a certain value thatallowing the simulated face region to be aligned with the targeted faceregion when the face is close enough to the display screen. Of course,the size of the simulated face region displayed on the display screen inreal time can also be adjusted to be aligned with the targeted faceregion only when the face to be verified is close enough to the displayscreen, and no limitation will be given herein. Both the targeted faceregion and the second preset ratio can be set as required, and nolimitation will be given here in the present disclosure.

For example, if the simulated face region of the face to be verified isdisposed in the targeted face region but the ratio of the size of thesimulation face are to the size of the targeted face region is less thanthe second preset ratio (e.g., two-thirds), the pose of the face to beverified can be too oblique and/or the face to be verified can be toofar away from the image acquisition device. In such a case, it can bedetermined that the image acquisition conditions do not satisfy therequirements.

Illustratively, the method for face liveness detection SE1200 (SE1300 orSE1400) can further comprise: outputting second acquisition promptinformation in real time to prompt the face to be verified to be closerto the image acquisition device if the ratio of the size of thesimulated face region to the size of the targeted face region is notgreater than the second preset ratio. For example, the secondacquisition prompt information can be outputted in the form of one ormore selected from voice, image and text. For example, if the ratio ofthe size of the simulated face region to the size of the targeted faceregion is not greater than the second preset ratio, prompt informationsuch as “Please be Closer to the Camera” (or “Please be Closer to theMobile Phone”) can be displayed on the display screen.

According to the embodiment of the present disclosure, the method SE1200(SE1300 or SE1400) can further comprise: determining the relativepositional relationship between the simulated face region and thetargeted face region in real time; and outputting third acquisitionprompt information in real time according to the relative positionalrelationship, so as to notify the change of the relative positionalrelationship and allow the simulated face region to be closer to thetargeted face region.

For example, when the method and device for face liveness detectionprovided by the embodiment of the present disclosure are implemented bythe mobile terminal, the simulated face region and the icon forindicating the targeted face region (namely the targeted face region)can be displayed in real time on the display screen of the mobileterminal. The real-time display of the simulated face region and theicon for indicating the targeted face region can provide convenience forthe user to know the state of the current image acquisition conditionand the difference between the current image acquisition condition andthe preset requirements, so that the user can conveniently adjust thepose of the user or the image acquisition device (or the device for faceliveness detection including the image acquisition device), and hencethe subsequent stage for face liveness detection can be entered as soonas possible. Therefore, the real-time display of the simulated faceregion and the icon for indicating the targeted face region can improvethe user experience and the efficiency of the face liveness detection.

For example, according to the embodiment of the present disclosure, inthe process of adopting the image acquisition device to acquire the faceimages, the method for face liveness detection SE1200 (SE1300 or SE1400)further comprises: outputting a brightness control instruction duringand/or before acquiring the real-time image of the face to be verified,in which the brightness control instruction is configured for setting abrightness of a display screen (for example, the display screen can beused for displaying the face to be verified) to be one of the following:to be a constant value which is greater than a preset brightnessthreshold but less than a maximum brightness of the display screen, tobe a constant value which is equal to the maximum brightness of thedisplay screen, and to be changed along with an intensity change ofambient light.

Because poor illumination condition can adversely affect the quality ofthe face images, the method and device for face liveness detection canfail to work properly in situations with poor illumination conditions.Illustratively, in the process of adopting the image acquisition deviceto acquire the face images, the setting of the brightness of the displayscreen can be changed (for example, the brightness of the display screencan be increased), for example, the brightness of the display screen canset to be the constant value which is greater than the preset brightnessthreshold but less than the maximum brightness of the display screen, orthe brightness of the display screen can set to be the constant valuewhich is equal to the maximum brightness of the display screen, suchthat the light of the display screen can be used to illuminate the faceto be verified, and hence the face liveness detection still can beperformed in a dark environment. For example, the above-mentionedworking mode can fully utilize the self-luminescence characteristic ofthe display screen of the mobile terminal, and therefore, the lightemitted by the display screen can be adopted as complementary light toilluminate the face to be verified, ensures that the face images withgood quality can be still acquired in dark environments, and thus allowsthe method for face liveness detection to be robust. Optionally, themobile terminal can have the capability to detect ambient light, suchthat the brightness of the display screen can be automatically adjustedaccording to the intensity change of the ambient light.

In the above-mentioned three embodiments of determining the imageacquisition conditions of the face to be verified with reference toFIGS. 24, 25 and 26, the face to be verified or one part thereof isaligned with a reference position. However, it should be understood thatthe above-mentioned embodiments are not intended to limit the presentdisclosure, and the present disclosure can adopt other implementationmethods to determine the image acquisition condition of the face to beverified. For example, the image acquisition condition of the face to beverified can include the size and/or pose of the face to be verified inthe real-time image, while the position of the face to be verified inthe image is not adopted as a determination condition for the imageacquisition condition; in such a case, it can be determined that thepreset requirement of the image acquisition condition is satisfied aslong as the size of the face to be verified is in a preset size rangeand/or the pose of the face to be verified is in a preset pose range.For another example, the image acquisition condition of the face to beverified can include the position, the size and the pose of the face tobe verified in the real-time image, in such a case, it can be determinedthat the preset requirement of the image acquisition condition issatisfied in a case that the position of the face to be verified is in apreset position range, the size of the face to be verified is in apreset size range, and the pose of the face to be verified is in apreset pose range.

Detailed description will be given below to a device for face livenessdetection provided by the embodiment of the present disclosure withreference to FIG. 27. FIG. 27 is an illustrative block diagram of adevice for face liveness detection 1100. For example, the device forface liveness detection 1100 can be used for the face livenessdetection, and more specifically, the device for face liveness detection1100 can implement the foregoing method for face liveness detectionprovided by the embodiment of the present disclosure.

For example, as illustrated in FIG. 27, the device for face livenessdetection 1100 comprises one or more processors 1101, one or morestorages 1102, an input device 1103, an output device 1104, an imageacquisition device 1105 and a light source 1106. These components areconnected with each other through a bus system 1107 and/or connectingmechanisms (not shown) in other forms. It should be understood that thecomponents and the structures of the device for face liveness detection1100 as illustrated in FIG. 27 are only illustrative and notrestrictive, and the device for face liveness detection can alsocomprise other components and structures as required.

The processor 1101 can be a CPU or a processor in other forms havingdata processing capability and/or instruction execution capability, andother components in the device for face liveness detection 1100 can becontrolled by the processor 1101 to execute expected functions. Forexample, the processor 1101 can control the device for face livenessdetection 1100 to implement the foregoing method for face livenessdetection provided by the embodiment of the present disclosure.

The storage 1102 can include one or more computer program products, andthe computer program products can include computer readable storagemedia in various forms, e.g., volatile memories and/or nonvolatilememories. The volatile memory, for example, can include an RAM and/or acache. The nonvolatile memory, for example, can include an ROM, a harddisc, a flash memory, etc. One or more computer program instructions canbe stored on the computer readable storage medium, and the processor1101 can run the program instructions to realize the function of aclient and/or other expected functions (implemented by the processor) inthe following embodiments and/or the above-mentioned embodiments of thepresent disclosure. Various APPs and data, e.g., various data usedand/or produced by the APPs, can also be stored in the computer readablestorage medium.

The input device 1103 can be a device used by the user for inputting aninstruction and the input device 1103 can include one or more selectedfrom a keyboard, a mouse, a microphone and a touch screen.

The output device 1104 can output various kinds of information (e.g.,image and/or voice) to the outside (e.g., the user), and the outputdevice 1104 can include one or more selected from a display screen and aloudspeaker, but the embodiment of the present disclosure is not limitedthereto.

The image acquisition device 1105 can acquire various kinds of images,e.g., face images (or face video), and the acquired images are stored inthe storage 1102 and can be used by other components of the device forface liveness detection 1100. The image acquisition device 1105 can be acamera. It should be understood that the case that the device for faceliveness detection 1100 comprises the image acquisition device 1105 isonly illustrative, and the device for face liveness detection 1100 canalso comprise no image acquisition device 1105, in such a case, otherimage acquisition devices separated from the face liveness detectionsystem 1100 can be utilized to acquire images and then the acquiredimages can be send to the device for face liveness detection 1100.

The light source 1106 can be an apparatus capable of emitting light. Forexample, the light source 1106 can include a special purpose lightsource such as LEDs, and can also include an unconventional light sourcesuch as a display screen. When the method and device for face livenessdetection are implemented by a mobile terminal such as a smart mobilephone, the input device 1103, the output device 1104 and the lightsource 1106 can be implemented by same display screen.

For example, the device for face liveness detection 1100 can beimplemented on equipment such as a personal computer or a remote server,but the embodiment of the present disclosure is not limited thereto.

According to another aspect of the present disclosure, a face livenessdetection device is provided. FIG. 28 is an illustrative block diagramof a face liveness detection device 1500 provided by an embodiment ofthe present disclosure.

As illustrated in FIG. 28, the face liveness detection device 1500provided by the embodiment of the present disclosure comprises anillumination image acquisition device 1510, an illumination livenessdetection device 1520 and a passing determination device for faceliveness detection 1530. The above-mentioned devices of the device forface liveness detection 1500 are cooperated with each other to realizethe above-mentioned function for face liveness detection in theembodiments of the present disclosure.

For example, the illumination image acquisition device 1510 can beconfigured to acquire a plurality of illumination images of the face tobe verified. For example, at least partial functions of the illuminationimage acquisition device 1510 can be realized by the programinstructions stored in the storage 1102, run by the processor 1101 inthe device for face liveness detection as illustrated in FIG. 27.

For example, the illumination liveness detection device 1520 can beconfigured to determine whether or not a face to be verified passes theillumination liveness detection according to the light reflectioncharacteristic of the face to be verified in one or more illuminationimages, and obtain an illumination liveness detection result. Forexample, the illumination liveness detection device 1520 can beimplemented by the program instructions stored in the storage 1102, runby the processor 1101 of the device for face liveness detection asillustrated in FIG. 27.

For example, the passing determination device for face livenessdetection 1530 can be configured to determine whether or not the face tobe verified passes the face liveness detection at least according to theillumination liveness detection result. For example, the passingdetermination device for face liveness detection 1530 can be implementedby the program instructions stored in the storage 1102, run by theprocessor 1101 in the device for face liveness detection 1101 asillustrated in FIG. 27.

FIG. 29 is an illustrative block diagram of a face liveness detectionsystem 1600 provided by an embodiment of the present disclosure. Forexample, the face liveness detection system 1600 comprises an imageacquisition device 1610, a storage 1620, a processor 1630 and a lightsource 1640.

For example, the light source 1640 is configured to dynamically changethe mode of illumination light irradiated on the face to be verified.For example, the image acquisition device 1610 can be configured toacquire images (including illumination images, action images andreal-time images) of the face to be verified. For example, the imageacquisition device 1610 is optional. For example, the face livenessdetection system 1600 can comprise no image acquisition device; in sucha case, other image acquisition devices separated from the face livenessdetection system 1600 can be utilized to acquire images for faceliveness detection and the acquired images can be sent to the faceliveness detection system 1600.

For example, the storage 1620 is configured to store computer programinstructions used for implementing corresponding steps of the method forface liveness detection provided by the embodiment of the presentdisclosure.

For example, the processor 1630 is configured to run the computerprogram instructions stored in the storage 1620, so as to executecorresponding steps of the method for face liveness detection providedby the embodiment of the present disclosure. For example, theillumination liveness detection device 1520 and the passingdetermination device for face liveness detection 1530 of the device forface liveness detection 1500 provided by the embodiment of the presentdisclosure can be implemented by the processor 1630.

For example, in one embodiment, when the computer program instructionsare run by the processor 1630, the face liveness detection system 1600executes the following steps: obtaining an illumination livenessdetection result according to the light reflection characteristic of theface to be verified in the plurality of illumination images; anddetermining whether or not the face to be verified passes the faceliveness detection at least according to the illumination livenessdetection result.

FIG. 30 shows a storage medium 1700 in the embodiment of the presentdisclosure. Program instructions 1710 (namely computer programinstructions) are stored in the storage medium 1700. When the programinstructions 1710 are run by a computer or a processor, the storagemedium can be configured to execute corresponding steps in the methodfor face liveness detection provided by the embodiment of the presentdisclosure, and can also be configured to implement correspondingdevices, modules, sub-modules or units in the device for face livenessdetection provided by the embodiments of the present disclosure. For thesake of clarity, corresponding content is appropriately omitted here.The storage medium, for example, can include a memory card of a smartmobile phone, a storage of a tablet PC, a hard disc of a personalcomputer, an ROM, an erasable programmable read-only memory (EPROM), acompact disc-read-only memory (CD-ROM), a USB memory or any combinationof the above-mentioned storage media.

For example, in one example, when the computer program instructions arerun by the computer or the processor, the computer or the processor canbe implemented as various devices, modules, sub-modules, and/or units ofthe device for face liveness detection provided by the embodiment of thepresent disclosure, and/or can execute the method for face livenessdetection provided by the embodiment of the present disclosure.

In one embodiment, the computer program instructions are used forexecuting the following steps in the process of running: acquiring aplurality of illumination images, which are respectively correspondingto various modes, of the face to be verified captured in the process ofdynamically changing the mode of the illumination light irradiated onthe face to be verified; obtaining an illumination liveness detectionresult according to a light reflection characteristic of the face to beverified in the plurality of illumination images; and determiningwhether or not the face to be verified passes the face livenessdetection at least according to the illumination liveness detectionresult. For example, the step of obtaining the illumination livenessdetection result according to the light reflection characteristic of theface to be verified in the plurality of illumination images can include:analyzing the plurality of illumination images, acquiring the lightreflection characteristic of the face to be verified in the plurality ofillumination images, and obtaining the illumination liveness detectionresult.

Relevant devices, modules, sub-modules and/or or units in the system forface liveness detection 1600 provided by the embodiment of the presentdisclosure can be implemented by the computer program instructionsstored in the memory, run by the processor of the device for faceliveness detection provided by the embodiment of the present disclosurefor implementing the face liveness detection, or can be implemented inthe process of adopting the computer to run the computer instructionsstored in the computer-readable storage medium of the computer programproduct in the embodiment of the present disclosure.

FIG. 31 is an illustrative block diagram of a device for face livenessdetection 1900 provided by the embodiment of the present disclosure.

As illustrated in FIG. 31, the device for face liveness detection 1900provided by the embodiment of the present disclosure comprises acondition determination device 1910, a face image acquisition device1920 and a passing determination device for face liveness detection1930. The above-mentioned devices can be used for executing varioussteps/functions of the method for face liveness detection provided bythe embodiment of the present disclosure. Description will be givenbelow to main functions of the components of the device for faceliveness detection 1900, and the details already described above areomitted.

For example, the condition determination device 1910 is configured todetermine whether or not a preset requirement of an image acquisitioncondition of the face to be verified is satisfied, in which the imageacquisition condition at least comprises one or more selected from aposition of the face to be verified, a pose of the face to be verifiedand a size of the face to be verified in a real-time image acquired byan image acquisition device. For example, at least partial functions ofthe condition determination device 1910 can be implemented by theprogram instructions, stored in the storage (for example, the storage104 as illustrated in FIG. 27), run by the processor (for example, theprocessor 102 in the device for face liveness detection as illustratedin FIG. 27).

For example, the face image acquisition device 1920 is configured toacquire face images of the face to be verified acquired by the imageacquisition device when the preset requirement of the image acquisitioncondition is satisfied. For example, at least partial functions of theface image acquisition device 1920 can be implemented by the programinstructions, stored in the storage (for example, the storage 104 asillustrated in FIG. 27), run by the processor (for example, theprocessor 102 in the device for face liveness detection as illustratedin FIG. 27).

For example, the passing determination device for face livenessdetection 1930 is configured to determine whether or not the face to beverified passes the face liveness detection according to the faceimages. For example, the passing determination device for face livenessdetection 1930 can be implemented by the program instructions, stored inthe storage (for example, the storage 104 as illustrated in FIG. 27),run by the processor (for example, the processor 102 in the device forface liveness detection as illustrated in FIG. 27).

For example, in one example, the condition determination device 1910 caninclude a first real-time image acquisition sub-module, a first regiondisplay sub-module, a first prompt display sub-module and a firstcondition determination sub-module. For example, the first real-timeimage acquisition sub-module can be configured to acquire the real-timeimage of the face to be verified acquired by the image acquisitiondevice; the first region display sub-module can be configured to displaya face preview region in real time, and to display part of the real-timeimage in the face preview region in real time, in which, the displayedpart of the real-time image is consistent with the face preview regionin position; the first prompt display sub-module can be configured tooutput adjustment prompt information in real time according to the imageacquisition conditions of the face to be verified in the real-timeimage, and for example, the adjustment prompt information can be usedfor notifying the face to be verified to make adjustment allowing thepreset requirement of the image acquisition condition to be satisfied;the first condition determination sub-module can be configured todetermine whether or not the preset requirement of the image acquisitioncondition of the face to be verified is satisfied at least according towhether or not the position of the face to be verified in the real-timeimage falls within a range defined by the face preview region.

Illustratively, the first prompt display sub-module includes one or moreof the following: a first prompt output device configured to outputadjustment prompt information in a case that there is no face to beverified in the real-time image, so as to notify the person having theface to be verified to move toward a direction allowing the face to beverified to be in the real-time image; and a second prompt output deviceconfigured to output adjustment prompt information in a case that theposition of the face to be verified in the real-time image is deviatedfrom a face preview region, so as to notify the person having the faceto be verified to move towards a direction opposite to a deviationdirection.

Illustratively, the image acquisition condition can further include ablurriness of the real-time image and a shielding state of the face tobe verified in the real-time image; and the first prompt displaysub-module also includes one or more of the following: a third promptoutput device configured to output adjustment prompt information in acase that a blurriness of the real-time image exceeds a presetblurriness threshold, so as to notify user to clean the imageacquisition device; a fourth prompt output device configured to outputadjustment prompt information in a case that the pose of the face to beverified in the real-time image is in a face upward state, so as tonotify the person having the face to be verified to lower his/her head;a fifth prompt output device configured to output adjustment promptinformation in a case that the pose of the face to be verified in thereal-time image is in a face downward state, so as to notify the personhaving the face to be verified to raise his/her head; a sixth promptoutput device configured to output adjustment prompt information in acase that the pose of the face to be verified in the real-time image istilting to the left or the right, so as to notify the person having theface to be verified to look straight ahead; a seventh prompt outputdevice configured to output adjustment prompt information in a case thatthe size of the face to be verified in the real-time image is less thana first threshold, so as to notify the person having the face to beverified to be closer to the image acquisition device; an eighth promptoutput device configured to output adjustment prompt information in acase that the size of the face to be verified in the real-time image isgreater than a second threshold, so as to notify the person having theface to be verified to be away from the image acquisition device; and aninth prompt output device configured to output adjustment promptinformation in a case that a specific facial part of the face to beverified in the real-time image is shielded by an occlusion, so as tonotify the person having the face to be verified to remove the occlusionand to expose the specific facial part.

Illustratively, the adjustment prompt information can be displayed in anarea above the face preview region.

For example, in another example, the condition determination device 1910can include a second real-time image acquisition sub-module, a secondregion display sub-module and a second condition determinationsub-module. For example, the second real-time image acquisitionsub-module can be configured to acquire the real-time image of the faceto be verified acquired by the image acquisition device; the secondregion display sub-module can be configured to display a targeted partregion in real time, and display a face to be verified of the real-timeimage in real time; and the second condition determination sub-modulecan be configured to determine whether or not the position of a specificfacial part of the face to be verified in the real-time image fallswithin the targeted part region, determine that the preset requirementof the image acquisition condition is satisfied if so, and determiningthat the preset requirement of the image acquisition condition of theface to be verified is not satisfied if not.

For example, in still another example, the condition determinationdevice 1910 can include a third real-time image acquisition sub-module,a third region display sub-module, a fourth region display sub-moduleand a third condition determination sub-module. For example, the thirdreal-time image acquisition sub-module can be configured to acquire thereal-time image of the face to be verified acquired by the imageacquisition device; the third region display sub-module can beconfigured to display a simulated face region changing along with theface to be verified in real time according to the image acquisitionconditions of the face to be verified in the real-time image, and theface to be verified is displayed in the simulated face region; thefourth region display sub-module can be configured to display a targetedface region for indicating an alignment of the face to be verified inreal time; and the third condition determination sub-module can beconfigured to determine whether or not the simulated face region isaligned with the targeted face region, in which it is determined thatthe preset requirement of the image acquisition condition of the face tobe verified is satisfied in a case that the simulated face region isaligned with the targeted face region, and it is determined that thepreset requirement of the image acquisition condition of the face to beverified is not satisfied in a case that the simulated face region isnot aligned with the targeted face region.

For example, in one example, the third condition determinationsub-module can include a first alignment determination unit and a secondalignment determination unit. For example, the first alignmentdetermination unit can be configured to determine that the simulatedface region is aligned with the targeted face region when the simulatedface region is disposed in the targeted face region and the ratio of thesize of the simulated face region to the size of the real-time image isgreater than first preset ratio. For example, the second alignmentdetermination unit can be configured to determine that the simulatedface region is not aligned with the targeted face region when thesimulated face region is not disposed in the targeted face region orwhen the ratio of the size of the simulated face region to the size ofthe real-time image is not greater than the first preset ratio.

Illustratively, the device for face liveness detection 1900 can furthercomprise a first prompt output module. For example, the first promptoutput module can be configured to output first acquisition promptinformation in real time to prompt the face to be verified to be closerto the image acquisition device when the ratio of the size of thesimulated face region to the size of the real-time image is not greaterthan the first preset ratio.

For example, in another example, the third condition determinationsub-module can include a third alignment determination unit and a fourthalignment determination unit. For example, the third alignmentdetermination unit can be configured to determine that the simulatedface region is aligned with the targeted face region when the simulatedface region is disposed in the targeted face region and the ratio of thesize of the simulated face region to the size of the targeted faceregion is greater than second preset ratio; and the fourth alignmentdetermination unit can be configured to determine that the simulatedface region is not aligned with the targeted face region when thesimulated face region is not disposed in the targeted face region orwhen the ratio of the size of the simulated face region to the size ofthe targeted face region is not greater than the second preset ratio.

Illustratively, the device for face liveness detection 1900 can furthercomprise a second prompt output module. For example, the second promptoutput module can be configured to output second acquisition promptinformation to prompt the face to be verified to be closer to the imageacquisition device when the ratio of the size of the simulated faceregion to the size of the targeted face region is not greater than thesecond preset ratio.

Illustratively, the device for face liveness detection 1900 can furthercomprise a positional relationship determination module and a thirdprompt output module. For example, the positional relationshipdetermination module can be configured to determine the relativepositional relationship between the simulated face region and thetargeted face region in real time; and the third prompt output modulecan be configured to output third acquisition prompt information in realtime according to the relative positional relationship, so as to notifythe change of the relative positional relationship and allow thesimulated face region to be closer to the targeted face region.

Illustratively, the device for face liveness detection 1900 can furthercomprise a brightness control module. For example, the brightnesscontrol module can be configured to output a brightness controlinstruction during and/or before acquiring the real-time image of theface to be verified, in which the brightness control instruction isconfigured for setting a brightness of a display screen, which is usedfor displaying the face to be verified, to be one of the following: tobe a constant value which is greater than a preset brightness thresholdbut less than a maximum brightness of the display screen, to be aconstant value which is equal to the maximum brightness of the displayscreen, and to be changed along with an intensity change of ambientlight

Illustratively, the face images can include one or more illuminationimages of the face to be verified. For example, the passingdetermination device for face liveness detection 1930 can include anillumination liveness detection sub-module and a passing determinationsub-module for face liveness detection. For example, the illuminationliveness detection sub-module can be configured to determine whether ornot the face to be verified passes the illumination liveness detectionaccording to the light reflection characteristic of the face to beverified in one or more illumination images, so as to obtain anillumination liveness detection result; and the passing determinationsub-module for face liveness detection can be configured to determinewhether or not the face to be verified passes the face livenessdetection at least according to the illumination liveness detectionresult.

Illustratively, the face image acquisition device 1920 can include acontrol instruction output sub-module. For example, the controlinstruction output sub-module can be configured to output a detectionlight control instruction when the image acquisition conditions of theface to be verified in the current image satisfy the presetrequirements, so as to control the light source to emit detection lighttoward the face to be verified; and to acquire one or more illuminationimages.

Illustratively, the face image acquisition device 1920 can include anenable sub-module. For example, the enable sub-module can be configuredto enable the condition determination device 1910 when the face to beverified moves and a moving distance is beyond an allowable range in theprocess of acquiring the illumination images.

Illustratively, the device for face liveness detection 1900 can furthercomprise an action instruction output module, an action imageacquisition module, an action detection module and an action detectionresult acquisition module. For example, the action instruction outputmodule is configured to output an action instruction, and the actioninstruction can be used for indicating the face to be verified toexecute the action corresponding to the action instruction; the actionimage acquisition module can be configured to acquire action images ofthe face to be verified acquired by the image acquisition device; theaction detection module can be configured to detect the action executedby the face to be verified according to the action images; and theaction detection result acquisition module can be configured to obtainan action liveness detection result according to the action detectionresult and the action instruction. For example, in such a case, thepassing determination sub-module for face liveness detection can includea passing determination unit for face liveness detection. For example,the passing determination unit for face liveness detection can beconfigured to determine whether or not the face to be verified passesthe face liveness detection according to both of the illuminationliveness detection result and the action liveness detection result.Illustratively, the action detection result acquisition module caninclude an action detection result acquisition sub-module. For example,the action detection result acquisition sub-module can be configured todetermine that the face to be verified passes the action livenessdetection in a case that an action, which is executed by the face to beverified and matched with the action instruction, is detected in theaction image, which is acquired within a time period not greater than apreset time period of the action liveness detection, and to determinethat the face to be verified fails to passes the action livenessdetection in a case that the action, which is executed by the face to beverified and matched with the action instruction, is not detected in theaction image, which is acquired within the time period not greater thanthe preset time period of the action liveness detection.

Illustratively, the device for face liveness detection 1900 can furthercomprise a fourth prompt output module. For example, the fourth promptoutput module is configured to output first time prompt information inthe process of outputting the action instruction. The first time promptinformation includes count-down information corresponding to the presettime period of the action liveness detection.

Illustratively, the device for face liveness detection 1900 can furthercomprise a fifth prompt output module. For example, the fifth promptoutput module can be configured to output second prompt information inthe process and/or before performing the illumination livenessdetection. For example, the second prompt information can be used fornotifying the face to be verified to keep still within the preset timeperiod of the illumination liveness detection. Illustratively, thesecond prompt information can include count-down informationcorresponding to the preset time period of the illumination livenessdetection.

Illustratively, the device for face liveness detection 1900 can furthercomprise a sixth prompt output module. For example, the sixth promptoutput module can be configured to output first prompt informationbefore the condition determination device 1910 determines that thepreset requirement of the image acquisition condition of the face to beverified is satisfied, in which the first prompt information is used fornotifying the face to be verified to be directly opposite to the imageacquisition device and to be closer to the image acquisition device.

Illustratively, the sixth prompt output module can further include asecond prompt output sub-module. For example, the second prompt outputsub-module can be configured to output first prompt information in oneor more forms selected from voice, image and text.

FIG. 32 is an illustrative block diagram of a face liveness detectionsystem 1000 provided by the embodiment of the present disclosure. Theface liveness detection system 1000 can comprise an image acquisitiondevice 1010, a storage 1020, a processor 1030 and a light source 1040.The face liveness detection system 1000 is configured to executecorresponding steps of the method for face liveness detection providedby the embodiment of the present disclosure, and is configured toimplement corresponding devices, modules, sub-modules and/or units inthe device for face liveness detection provided by the embodiment of thepresent disclosure. For the sake of clarity, corresponding content isappropriately omitted here.

For example, the image acquisition device 1010 can be configured toacquire images (including illumination images, action images andreal-time images) of the face to be verified. For example, the imageacquisition device 1010 is optional. The face liveness detection system1000 can comprise no image acquisition device. In such a case, otherimage acquisition devices separated from the face liveness detectionsystem 1000 can be utilized to acquire images for face livenessdetection and then send the acquired images to the face livenessdetection system 1000.

For example, the storage 1020 stores computer program instructions whichcan be used for implementing corresponding steps of the method for faceliveness detection provided by the embodiment of the present disclosure.

For example, the processor 1030 can be configured to run the computerprogram instructions stored in the storage 1020, so as to executecorresponding steps of the method for face liveness detection providedby the embodiment of the present disclosure, and can be configured toimplement the condition determination device 1910, the face imageacquisition device 1920 and the passing determination device for faceliveness detection 1930 of the device for face liveness detection 1900provided by the embodiment of the present disclosure.

For example, the light source 1040 can be configured to emit detectionlight to the face to be verified. For example, the light source 1040 isoptional. The face liveness detection system 1000 can comprise no lightsource.

For example, in one embodiment, when the computer program instructionsare run by the processor 1030, the face liveness detection system 1000executes the following steps.

S1210: determining whether or not a preset requirement of an imageacquisition condition of the face to be verified is satisfied, in whichthe image acquisition condition at least comprises one or more selectedfrom a position of the face to be verified, a pose of the face to beverified and a size of the face to be verified in a real-time imageacquired by an image acquisition device;

S1220: acquiring face images of the face to be verified when the presetrequirement of the image acquisition condition is satisfied; and

S1230: determining whether or not the face to be verified passes theface liveness detection according to the face images.

The embodiment of the present disclosure can further provide a storagemedium. For example, program instructions are stored on the storagemedium. When the program instructions are run by a computer or aprocessor, the storage medium is configured to execute correspondingsteps of the method for face liveness detection provided by theembodiment of the present disclosure, and is configured to implementcorresponding devices, modules, sub-modules and/or units of the devicefor face liveness detection provided by the embodiment of the presentdisclosure. For the sake of clarity, corresponding content isappropriately omitted here. The storage medium, for example, can includea memory card of a smart mobile phone, a storage of a tablet PC, a harddisc of a personal computer, an ROM, an EPROM, a CD-ROM, a USB memory orany combination of the above-mentioned storage media.

For example, in one embodiment, when the program instructions are run bythe computer or the processor, the computer or the processor can beadopted to implement various devices, modules, sub-modules and/or orunits of the device for face liveness detection provided by theembodiment of the present disclosure, and/or can execute the method forface liveness detection provided by the embodiment of the presentdisclosure.

For example, in one embodiment, the program instructions are used forexecuting the following steps in the process of running. S1210:determining whether or not a preset requirement of an image acquisitioncondition of the face to be verified is satisfied, in which the imageacquisition condition at least comprises one or more selected from aposition of the face to be verified, a pose of the face to be verifiedand a size of the face to be verified in a real-time image acquired byan image acquisition device; S1220: acquiring face images of the face tobe verified acquired by the image acquisition device when the presetrequirement of the image acquisition condition is satisfied; and S1230:determining whether or not the face to be verified passes the faceliveness detection according to the face images.

At least part of the devices, modules, sub-modules, and/or units in theface liveness detection system provided by the embodiment of the presentdisclosure can be implemented by the computer program instructionsstored in the memory, run by the processor of the device for faceliveness detection for implementing face liveness detection provided bythe embodiment of the present disclosure, or can be implemented when thecomputer runs the computer program instructions stored in the computerreadable storage medium of the computer program product in theembodiment of the present disclosure. The method for face livenessdetection, device and system and the storage medium, provided by theembodiment of the present disclosure, can effectively counteract screenattackers, photo attackers or mask attackers and hence can improve thesafety and the user experience of an authentication system or similarsystems employing the method, the device, the system or the storagemedium for face liveness detection.

Although description has been given to the concrete embodiments withreference to the accompanying drawings, it should be understood that theabove-mentioned concrete embodiments are only illustrative and notintended to limit the scope of the present disclosure thereto. Variouschanges and modifications can be made by those skilled in the artwithout departing from the scope and the spirit of the presentdisclosure. All the changes and modifications shall fall within thescope of the present disclosure as claimed in the appended claims.

It should be understood by those skilled in the art that the componentsand the algorithm steps in the examples of the embodiment of the presentdisclosure can be implemented by electronic hardware or a combination ofcomputer software and the electronic hardware. Whether or not thefunctions are executed by hardware or software depends on specificimplementation and design constraints of the technical solution.Different methods can be adopted by those skilled in the art to realizethe described function for each specific implementation, but theabove-mentioned different methods for realizing the described functionfor each specific implementation shall not be construed as exceeding thescope of the present disclosure.

In several embodiments of the application, it should be understood thatthe disclosed equipment and method can be implemented by other means.For example, the foregoing device embodiments are only illustrative. Forexample, the division of the units is merely one kind of divisionaccording to logical function, and there can be other division modes inactual implementation. For example, a plurality of units or componentscan be combined or integrated into another device, or somecharacteristics can be omitted or not executed.

A large amount of concrete details have been provided in the descriptionherein. However, it should be understood that the embodiments of thepresent disclosure can be implemented without the concrete details. Thewell-known methods, structures and techniques are not shown in detail insome embodiments, so as not to obscure the understanding of thedescription.

Similarly, it should be understood that: in order to simplify thepresent disclosure and aid in the understanding of one or more ofvarious disclosure aspects, in the description of the concreteembodiments of the present disclosure, the features of the presentdisclosure are sometimes grouped together in a single embodiment, afigure or description thereof. However, the method provided by thepresent disclosure should not be construed as reflecting the followingcontent: namely the claimed present disclosure requires more featuresthan those expressly recited in each claim. More precisely, as reflectedby corresponding claims, corresponding technical problem can be solvedby features fewer than all the features of a single embodiment of thepresent disclosure. Thus, the claims following the Detailed Descriptionare hereby expressly incorporated into the Detailed Description, witheach claim as a separate embodiment of the present disclosure.

It should be understood by those skilled in the art that: in addition tothe features conflicting with each other, all the features of thepresent disclosure in the description (including the accompanyingclaims, abstract and drawings) and all the disclosed processes or unitsof any method or device can be combine. Unless otherwise specificallystated, each feature disclosed in the description (including theaccompanying claims, abstract and drawings) can be replaced by analternative feature serving the same, equivalent or similar purpose.

In addition, it should be understood by those skilled in the art thatalthough some embodiments described here include some features includedin other embodiments but not include other features, combinations offeatures of different embodiments are meant to fall within the scope ofthe present disclosure and form different embodiments. For example, anyof the claimed embodiments in the claims can be used in any combination.

Various components, devices, modules, sub-modules and/or units providedby the embodiments of the present disclosure can be implemented byhardware or software modules run by one or more processors or acombination of hardware and software. It should be understood by thoseskilled in the art that part of or all of the functions of somecomponents, devices, modules, sub-modules and/or units in the device forface liveness detection provided by the embodiment of the presentdisclosure can be realized by a microprocessor or a DSP. The presentdisclosure can also be implemented as a part or all of the deviceprograms (for example, computer programs and computer program products)for executing the method described herein. The programs for implementingthe present disclosure can be stored on a computer readable medium orcan be in the form of one or more signals. Such signals can bedownloaded from the Internet website, provided on a carrier signal, orprovided in any other form.

It should be noted that the above-mentioned embodiments are intended toillustrate the present disclosure but not intended to limit the presentdisclosure, and alternative embodiments can be designed by those skilledin the art without departing from the scope of the appended claims. Inthe claims, any reference numeral placed between brackets shall not beconstrued as the limitation of the claims. The word “comprising” doesnot exclude the presence of elements or steps not listed in the claims.The word “a” or “an” preceding an element does not exclude the presenceof a plurality of such elements. The present disclosure can beimplemented with the aid of hardware comprising several differentelements and by means of a suitably programmed computer. In the unitclaims enumerating several apparatuses, several of these apparatuses canbe specifically embodied by the same hardware. The use of the wordsfirst, second, third and the like does not indicate any order, the wordsfirst, second, third and the like can be interpreted as name.

The foregoing is only the concrete embodiments of the present disclosureor the description of the concrete embodiments. The scope of protectionof the present disclosure is not limited thereto. Any change orreplacement that can be easily thought of by those skilled in the artwithin the technical scope disclosed by the present disclosure shallfall within the scope of the present disclosure. The scope of thepresent disclosure shall be defined by the appended claims.

What is claimed is:
 1. A method for face liveness detection, comprising:performing an illumination liveness detection and obtaining anillumination liveness detection result; and determining whether or not aface to be verified passes the face liveness detection at leastaccording to the illumination liveness detection result; whereinperforming of the illumination liveness detection and obtaining of theillumination liveness detection result comprise: acquiring a pluralityof illumination images of the face to be verified, wherein the pluralityof illumination images are captured in a process of dynamically changingmode of illumination light irradiated on the face to be verified and arerespectively corresponding to various modes of the illumination light;and obtaining the illumination liveness detection result according to alight reflection characteristic of the face to be verified in theplurality of illumination images.
 2. The method for face livenessdetection according to claim 1, wherein acquiring of the plurality ofillumination images of the face to be verified comprises: dynamicallychanging the mode of the illumination light irradiated on the face to beverified, and capturing the plurality of illumination images, which arerespectively corresponding to the various modes of the illuminationlight, of the face to be verified; obtaining of the illuminationliveness detection result according to the light reflectioncharacteristic of the face to be verified in the plurality ofillumination images comprises: analyzing the plurality of illuminationimages, acquiring the light reflection characteristic of the face to beverified in the plurality of illumination images, and obtaining theillumination liveness detection result according to the light reflectioncharacteristic; and dynamically changing the mode of the illuminationlight irradiated on the face to be verified comprises: dynamicallychanging color and/or position of the illumination light.
 3. The methodfor face liveness detection according to claim 2, wherein light emittedfrom a display screen is used as the illumination light irradiated onthe face to be verified; and a mode of the light emitted from thedisplay screen is dynamically changed by changing contents displayed onthe display screen, so that the mode of the illumination lightirradiated on the face to be verified is dynamically changed.
 4. Themethod for face liveness detection according to claim 1, furthercomprising: performing an action liveness detection before determiningwhether or not the face to be verified passes the face livenessdetection; wherein performing of the action liveness detectioncomprises: outputting an action instruction used for notifying the faceto be verified to execute an action corresponding to the actioninstruction; acquiring an action image of the face to be verified;detecting the action executed by the face to be verified on the basis ofthe action image, so as to obtain an action detection result; andobtaining an action liveness detection result according to the actiondetection result and the action instruction; and determining of whetheror not the face to be verified passes the face liveness detection atleast according to the illumination liveness detection result comprises:determining whether or not the face to be verified passes the faceliveness detection according to both of the illumination livenessdetection result and the action liveness detection result.
 5. The methodfor face liveness detection according to claim 4, wherein number oftimes for performing the action liveness detection is increased by onefor each performance of the action liveness detection, so as to obtainthe number of times for performing the action liveness detection; andafter the action liveness detection result is obtained and in a casethat the action liveness detection result indicates that the face to beverified fails to pass the action liveness detection, the method furthercomprises: outputting first error information used for notifying afailure of the action liveness detection; determining whether or not thenumber of times for performing the action liveness detection is greaterthan a first counting threshold; and determining whether or not the faceto be verified passes the face liveness detection at least according tothe illumination liveness detection result in a case that the number oftimes for performing the action liveness detection is greater than thefirst counting threshold, and performing the action liveness detectionagain in a case that the number of times for performing the actionliveness detection is not greater than the first counting threshold, orperforming the illumination liveness detection again in a case that theillumination liveness detection is performed before the action livenessdetection and the number of times for performing the action livenessdetection is not greater than the first counting threshold.
 6. Themethod for face liveness detection according to claim 1, furthercomprising: determining whether or not a preset requirement of an imageacquisition condition of the face to be verified is satisfied beforeperforming the illumination liveness detection, so as to perform theillumination liveness detection in a case that the preset requirement ofthe image acquisition condition is satisfied, wherein the imageacquisition condition at least comprises one or more selected from aposition of the face to be verified, a pose of the face to be verifiedand a size of the face to be verified in a real-time image acquired byan image acquisition device.
 7. The method for face liveness detectionaccording to claim 6, wherein the image acquisition condition furthercomprises a blurriness of the real-time image and a shielding state ofthe face to be verified in the real-time image.
 8. The method for faceliveness detection according to claim 6, wherein determining of whetheror not the preset requirement of the image acquisition condition of theface to be verified is satisfied comprises: adopting the imageacquisition device to acquire the real-time image of the face to beverified; displaying a reference part of the face to be verified in thereal-time image and a reference region in real time; and determiningwhether or not the preset requirement of the image acquisition conditionis satisfied at least according to whether or not the reference part ofthe face to be verified in the real-time image falls within thereference region.
 9. The method for face liveness detection according toclaim 8, wherein the reference part of the face to be verified in thereal-time image and the reference region are respectively the face to beverified in the real-time image and a face preview region; or thereference part of the face to be verified in the real-time image and thereference region are respectively a specific facial part of the face tobe verified in the real-time image and a targeted part region.
 10. Themethod for face liveness detection according to claim 8, whereindetermining of whether or not the preset requirement of the imageacquisition condition is satisfied at least according to whether or notthe reference part of the face to be verified in the real-time imagefalls within the reference region comprises: determining that the presetrequirement of the image acquisition condition is satisfied in a casethat the reference part of the face to be verified in the real-timeimage falls within the reference region; and determining that the presetrequirement of the image acquisition condition is not satisfied in acase that the reference part of the face to be verified in the real-timeimage fails to fall within the reference region.
 11. The method for faceliveness detection according to claim 8, wherein determining of whetheror not the preset requirement of the image acquisition condition issatisfied at least according to whether or not the reference part of theface to be verified in the real-time image falls within the referenceregion comprises: determining that the preset requirement of the imageacquisition condition is satisfied in a case that the reference part ofthe face to be verified in the real-time image falls within thereference region and a ratio of a size of the reference part of the faceto be verified to a size of the real-time image is greater than a ratiothreshold; and determining that the preset requirement of the imageacquisition condition is not satisfied in a case that the reference partof the face to be verified in the real-time image fails to fall withinthe reference region or the ratio of the size of the reference part ofthe face to be verified to the size of the real-time image is less thanor equal to the ratio threshold.
 12. The method for face livenessdetection according to claim 8, wherein determining of whether or notthe preset requirement of the image acquisition condition of the face tobe verified is satisfied further comprises: acquiring postureinformation of the image acquisition device; determining whether or notthe image acquisition device is vertically placed according to theposture information; and determining that the preset requirement of theimage acquisition condition is not satisfied in a case that the imageacquisition device is not vertically placed.
 13. The method for faceliveness detection according to claim 6, further comprising: outputtingadjustment prompt information in a case that the preset requirement ofthe image acquisition condition is not satisfied, wherein the adjustmentprompt information is used for notifying the face to be verified to makeadjustment allowing the preset requirement of the image acquisitioncondition to be satisfied.
 14. The method for face liveness detectionaccording to claim 13, wherein outputting of the adjustment promptinformation in the case that the preset requirement of the imageacquisition condition is not satisfied comprises: outputting theadjustment prompt information in a case that there is no face to beverified in the real-time image, so as to notify a person having theface to be verified to move toward a direction allowing the face to beverified to be in the real-time image; outputting the adjustment promptinformation in a case that the position of the face to be verified inthe real-time image is deviated from a face preview region, so as tonotify the person having the face to be verified to move towards adirection opposite to a deviation direction; outputting the adjustmentprompt information in a case that a blurriness of the real-time imageexceeds a preset blurriness threshold, so as to notify user to clean theimage acquisition device; outputting the adjustment prompt informationin a case that the pose of the face to be verified in the real-timeimage is in a face upward state, so as to notify the person having theface to be verified to lower his/her head; outputting the adjustmentprompt information in a case that the pose of the face to be verified inthe real-time image is in a face downward state, so as to notify theperson having the face to be verified to raise his/her head; outputtingthe adjustment prompt information in a case that the pose of the face tobe verified in the real-time image is tilting to the left or the right,so as to notify the person having the face to be verified to lookstraight ahead; outputting the adjustment prompt information in a casethat the size of the face to be verified in the real-time image is lessthan a first threshold, so as to notify the person having the face to beverified to be closer to the image acquisition device; outputting theadjustment prompt information in a case that the size of the face to beverified in the real-time image is greater than a second threshold, soas to notify the person having the face to be verified to be away fromthe image acquisition device; and outputting the adjustment promptinformation in a case that a specific facial part of the face to beverified in the real-time image is shielded by an occlusion, so as tonotify the person having the face to be verified to remove the occlusionand to expose the specific facial part.
 15. The method for face livenessdetection according to claim 6, wherein determining of whether or notthe preset requirement of the image acquisition condition of the face tobe verified is satisfied comprises: adopting the image acquisitiondevice to acquire the real-time image of the face to be verified;displaying a simulated face region changing along with the face to beverified in real time according to the image acquisition condition,wherein the face to be verified is displayed in the simulated faceregion; displaying in real time a targeted face region, which is usedfor indicating an alignment of the face to be verified; and determiningwhether or not the simulated face region is aligned with the targetedface region, wherein it is determined that the preset requirement of theimage acquisition condition of the face to be verified is satisfied in acase that the simulated face region is aligned with the targeted faceregion, and it is determined that the preset requirement of the imageacquisition condition of the face to be verified is not satisfied in acase that the simulated face region is not aligned with the targetedface region.
 16. The method for face liveness detection according toclaim 15, wherein determining of whether or not the simulated faceregion is aligned with the targeted face region comprises: determiningthat the simulated face region is aligned with the targeted face regionin a case that the simulated face region is within the targeted faceregion and a ratio of a size of the simulated face region to a size ofthe real-time image is greater than a first preset ratio; anddetermining that the simulated face region is not aligned with thetargeted face region in a case that the simulated face region is notwithin the targeted face region or the ratio of the size of thesimulated face region to the size of the real-time image is less than orequal to the first preset ratio.
 17. The method for face livenessdetection according to claim 15, wherein determining of whether or notthe simulated face region is aligned with the targeted face regioncomprises: determining that the simulated face region is aligned withthe targeted face region in a case that the simulated face region iswithin the targeted face region and a ratio of a size of the simulatedface region to a size of the targeted face region is greater than asecond preset ratio; and determining that the simulated face region isnot aligned with the targeted face region in a case that the simulatedface region is not within the targeted face region or the ratio of thesize of the simulated face region to the size of the targeted faceregion is less than or equal to the second preset ratio.
 18. The methodfor face liveness detection according to claim 8, further comprising:outputting first prompt information during and/or before acquiring thereal-time image of the face to be verified with the image acquisitiondevice, wherein the first prompt information is used for notifying theface to be verified to be directly opposite to the image acquisitiondevice and to be closer to the image acquisition device.
 19. The methodfor face liveness detection according to claim 1, further comprising:outputting second prompt information during and/or before acquiring theillumination images, wherein the second prompt information is used fornotifying the face to be verified to keep still within a preset timeperiod of the illumination liveness detection.
 20. The method for faceliveness detection according to claim 6, wherein determining whether ornot the preset requirement of the image acquisition condition of theface to be verified is satisfied again in a case that the face to beverified moves during acquiring the illumination images and a movingdistance is beyond an allowable range.
 21. The method for face livenessdetection according to claim 6, wherein number of times for performingthe illumination liveness detection is increased by one for eachperformance of the illumination liveness detection, so as to obtain thenumber of times for performing the illumination liveness detection; andafter the illumination liveness detection result is obtained, and in acase that the illumination liveness detection result indicates that theface to be verified fails to pass the illumination liveness detection,the method further comprises: outputting second error information usedfor notifying a failure of the illumination liveness detection;determining whether or not the number of times for performing theillumination liveness detection is greater than a second countingthreshold; and determining whether or not the face to be verified passesthe face liveness detection at least according to the illuminationliveness detection result in a case that the number of times forperforming the illumination liveness detection is greater than thesecond counting threshold, and determining whether or not the presetrequirement of the image acquisition condition of the face to beverified is satisfied or performing the illumination liveness detectionagain in a case that the number of times for performing the illuminationliveness detection is not greater than the second counting threshold.22. A device for face liveness detection, comprising: a processor, amemory and computer program instructions stored in the memory; whereinupon the processor running the computer program instructions, the devicefor face liveness detection performs a following method comprising:acquiring a plurality of illumination images of a face to be verified,wherein the plurality of illumination images are captured in a processof dynamically changing mode of illumination light irradiated on theface to be verified and are respectively corresponding to various modesof the illumination light; obtaining an illumination liveness detectionresult according to a light reflection characteristic of the face to beverified in the plurality of illumination images; and determiningwhether or not the face to be verified passes the face livenessdetection at least according to the illumination liveness detectionresult.
 23. A device for face liveness detection, comprising: a lightsource, an image acquisition device and a processing device, wherein thelight source is configured to dynamically change mode of illuminationlight irradiated on a face to be verified; the image acquisition deviceis configured to acquire a plurality of illumination images, which arerespectively corresponding to various modes of the illumination light,of the face to be verified; and the processing device is configured toobtain an illumination liveness detection result according to a lightreflection characteristic of the face to be verified in the plurality ofillumination images, and is further configured to determine whether ornot the face to be verified passes the face liveness detection at leastaccording to the illumination liveness detection result.
 24. The devicefor face liveness detection according to claim 23, further comprising anoutput device, wherein the output device is configured to output anaction instruction, wherein the action instruction is used for notifyingthe face to be verified to execute an action corresponding to the actioninstruction; the image acquisition device is further configured toacquire an action image of the face to be verified; the processingdevice is further configured to obtain an action detection result bydetecting the action executed by the face to be verified on the basis ofthe action image, and obtain an action liveness detection resultaccording to the action detection result and the action instruction; andthe processing device is further configured to determine whether or notthe face to be verified passes the face liveness detection according toboth of the illumination liveness detection result and the actionliveness detection result.
 25. The device for face liveness detectionaccording to claim 23, further comprising: a condition determinationdevice, wherein the image acquisition device is further configured toacquire a real-time image; the condition determination device isconfigured to determine whether or not an preset requirement of theimage acquisition condition of the face to be verified is satisfiedbefore acquiring the illumination images; and the image acquisitioncondition at least comprises one or more selected from a position of theface to be verified, a pose of the face to be verified and a size of theface to be verified, in the real-time image acquired by the imageacquisition device.